Bibliography

Abubakar Abid, Ali Abdalla, Ali Abid, Dawood Khan, Abdulrahman Alfozan, and James Y. Zou. 2019. Gradio: Hassle-free sharing and testing of ML models in the wild. ArXiv, abs/1906.02569.

Samira Abnar and Willem Zuidema. 2020. Quantifying attention flow in transformers. In Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel Tetreault, editors, Proceedings of the 58th annual meeting of the association for computational linguistics, pages 4190–4197, Online. Association for Computational Linguistics.

Reduan Achtibat, Sayed Mohammad Vakilzadeh Hatefi, Maximilian Dreyer, Aakriti Jain, Thomas Wiegand, Sebastian Lapuschkin, and Wojciech Samek. 2024. AttnLRP: Attention-aware layer-wise relevance propagation for transformers. In Proceedings of the 41st international conference on machine learning, Vienna, Austria. JMLR.org.

Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim. 2018. Sanity checks for saliency maps. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Advances in neural information processing systems, volume 31, pages 9505–9515, Montréal, Canada. Curran Associates, Inc.

Julius Adebayo, Michael Muelly, Harold Abelson, and Been Kim. 2022. Post hoc explanations may be ineffective for detecting unknown spurious correlation. In International conference on learning representations.

Julius Adebayo, Michael Muelly, Ilaria Liccardi, and Been Kim. 2020. Debugging tests for model explanations. In Proceedings of the 34th international conference on neural information processing systems, Red Hook, NY, USA. Curran Associates Inc.

Chirag Agarwal, Sree Harsha Tanneru, and Himabindu Lakkaraju. 2024. Faithfulness vs. Plausibility: On the (un)reliability of explanations from large language models. Arxiv.

Sweta Agrawal, António Farinhas, Ricardo Rei, and Andre Martins. 2024. Can automatic metrics assess high-quality translations? In Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen, editors, Proceedings of the 2024 conference on empirical methods in natural language processing, pages 14491–14502, Miami, Florida, USA. Association for Computational Linguistics.

Sweta Agrawal, Chunting Zhou, Mike Lewis, Luke Zettlemoyer, and Marjan Ghazvininejad. 2023. In-context examples selection for machine translation. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki, editors, Findings of the association for computational linguistics: ACL 2023, pages 8857–8873, Toronto, Canada. Association for Computational Linguistics.

Roee Aharoni, Melvin Johnson, and Orhan Firat. 2019. Massively multilingual neural machine translation. In Jill Burstein, Christy Doran, and Thamar Solorio, editors, Proceedings of the 2019 conference of the north American chapter of the association for computational linguistics: Human language technologies, volume 1 (long and short papers), pages 3874–3884, Minneapolis, Minnesota. Association for Computational Linguistics.

Arafat Ahsan, Vandan Mujadia, and Dipti Misra Sharma. 2021. Assessing post-editing effort in the English-Hindi direction. In Sivaji Bandyopadhyay, Sobha Lalitha Devi, and Pushpak Bhattacharyya, editors, Proceedings of the 18th international conference on natural language processing (ICON), pages 44–53, National Institute of Technology Silchar, Silchar, India. NLP Association of India (NLPAI).

J Alammar. 2021. Ecco: An open source library for the explainability of transformer language models. In Heng Ji, Jong C. Park, and Rui Xia, editors, Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing: System demonstrations, pages 249–257, Online. Association for Computational Linguistics.

Simone Alghisi, Massimo Rizzoli, Gabriel Roccabruna, Seyed Mahed Mousavi, and Giuseppe Riccardi. 2024. Should we fine-tune or RAG? Evaluating different techniques to adapt LLMs for dialogue. In Saad Mahamood, Nguyen Le Minh, and Daphne Ippolito, editors, Proceedings of the 17th international natural language generation conference, pages 180–197, Tokyo, Japan. Association for Computational Linguistics.

Duarte Miguel Alves, José Pombal, Nuno M Guerreiro, Pedro Henrique Martins, João Alves, Amin Farajian, Ben Peters, Ricardo Rei, Patrick Fernandes, Sweta Agrawal, Pierre Colombo, José G. C. de Souza, and Andre Martins. 2024. Tower: An open multilingual large language model for translation-related tasks. In First conference on language modeling.

Chantal Amrhein, Nikita Moghe, and Liane Guillou. 2022. ACES: Translation accuracy challenge sets for evaluating machine translation metrics. In Philipp Koehn, Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Tom Kocmi, André Martins, Makoto Morishita, et al., editors, Proceedings of the seventh conference on machine translation (WMT), pages 479–513, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.

Chantal Amrhein, Nikita Moghe, and Liane Guillou. 2023. ACES: Translation accuracy challenge sets at WMT 2023. In Philipp Koehn, Barry Haddow, Tom Kocmi, and Christof Monz, editors, Proceedings of the eighth conference on machine translation, pages 695–712, Singapore. Association for Computational Linguistics.

Chantal Amrhein and Rico Sennrich. 2021. How suitable are subword segmentation strategies for translating non-concatenative morphology? In Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih, editors, Findings of the association for computational linguistics: EMNLP 2021, pages 689–705, Punta Cana, Dominican Republic. Association for Computational Linguistics.

Chantal Amrhein and Rico Sennrich. 2022. Identifying weaknesses in machine translation metrics through minimum Bayes risk decoding: A case study for COMET. In Yulan He, Heng Ji, Sujian Li, Yang Liu, and Chua-Hui Chang, editors, Proceedings of the 2nd conference of the asia-pacific chapter of the association for computational linguistics and the 12th international joint conference on natural language processing (volume 1: Long papers), pages 1125–1141, Online only. Association for Computational Linguistics.

Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al. 2023. Palm 2 technical report. Arxiv.

Dana Arad, Yonatan Belinkov, Hanjie Chen, Najoung Kim, Hosein Mohebbi, Aaron Mueller, Gabriele Sarti, and Martin Tutek. 2025. Findings of the BlackboxNLP 2025 shared task: Localizing circuits and causal variables in language models. In Proceedings of the 8th BlackboxNLP workshop: Analyzing and interpreting neural networks for NLP, pages 543–552, Suzhou, China. Association for Computational Linguistics.

Andy Arditi, Oscar Obeso, Aaquib Syed, Daniel Paleka, Nina Panickssery, Wes Gurnee, and Neel Nanda. 2024. Refusal in language models is mediated by a single direction. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang, editors, Advances in neural information processing systems, volume 37, pages 136037–136083, Red Hook, NY, USA. Curran Associates, Inc.

Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, and Andrej Risteski. 2018. Linear algebraic structure of word senses, with applications to polysemy. Transactions of the Association for Computational Linguistics, 6:483–495.

Viraat Aryabumi, John Dang, Dwarak Talupuru, Saurabh Dash, David Cairuz, Hangyu Lin, Bharat Venkitesh, Madeline Smith, Jon Ander Campos, Yi Chern Tan, Kelly Marchisio, Max Bartolo, Sebastian Ruder, Acyr Locatelli, Julia Kreutzer, Nick Frosst, Aidan Gomez, Phil Blunsom, Marzieh Fadaee, et al. 2024. Aya 23: Open weight releases to further multilingual progress.

Akari Asai, Xinyan Yu, Jungo Kasai, and Hanna Hajishirzi. 2021. One question answering model for many languages with cross-lingual dense passage retrieval. Advances in Neural Information Processing Systems, 34:7547–7560.

Pepa Atanasova, Oana-Maria Camburu, Christina Lioma, Thomas Lukasiewicz, Jakob Grue Simonsen, and Isabelle Augenstein. 2023. Faithfulness tests for natural language explanations. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki, editors, Proceedings of the 61st annual meeting of the association for computational linguistics (volume 2: Short papers), pages 283–294, Toronto, Canada. Association for Computational Linguistics.

Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, and Isabelle Augenstein. 2020. A diagnostic study of explainability techniques for text classification. In Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu, editors, Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), pages 3256–3274, Online. Association for Computational Linguistics.

Giuseppe Attanasio, Eliana Pastor, Chiara Di Bonaventura, and Debora Nozza. 2023. Ferret: A framework for benchmarking explainers on transformers. In Danilo Croce and Luca Soldaini, editors, Proceedings of the 17th conference of the european chapter of the association for computational linguistics: System demonstrations, pages 256–266, Dubrovnik, Croatia. Association for Computational Linguistics.

Wilker Aziz, Sheila Castilho, and Lucia Specia. 2012. PET: A tool for post-editing and assessing machine translation. In Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Mehmet Uğur Doğan, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, and Stelios Piperidis, editors, Proceedings of the eighth international conference on language resources and evaluation (LREC‘12), pages 3982–3987, Istanbul, Turkey. European Language Resources Association (ELRA).

Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. 2016. Layer normalization. Arxiv Preprint.

Joris Baan, Nico Daheim, Evgenia Ilia, Dennis Ulmer, Haau-Sing Li, Raquel Fernández, Barbara Plank, Rico Sennrich, Chrysoula Zerva, and Wilker Aziz. 2023. Uncertainty in natural language generation: From theory to applications.

Alexander AND Montavon Bach Sebastian AND Binder. 2015. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLOS ONE, 10(7):1–46.

David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert Müller. 2010. How to explain individual classification decisions. J. Mach. Learn. Res., 11:1803–1831.

Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In Yoshua Bengio and Yann LeCun, editors, Proceedings of the 3rd international conference on learning representations (ICLR), San Diego, CA, USA.

Robert Baldock, Hartmut Maennel, and Behnam Neyshabur. 2021. Deep learning through the lens of example difficulty. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, and J. Wortman Vaughan, editors, Advances in neural information processing systems, volume 34, pages 10876–10889. Curran Associates, Inc.

Satanjeev Banerjee and Alon Lavie. 2005. METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. In Jade Goldstein, Alon Lavie, Chin-Yew Lin, and Clare Voss, editors, Proceedings of the ACL workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, pages 65–72, Ann Arbor, Michigan. Association for Computational Linguistics.

Fazl Barez, Tingchen Fu, Ameya Prabhu, Stephen Casper, Amartya Sanyal, Adel Bibi, Aidan O’Gara, Robert Kirk, Ben Bucknall, Tim Fist, Luke Ong, Philip Torr, Kwok-Yan Lam, Robert Trager, David Krueger, Sören Mindermann, José Hernandez-Orallo, Mor Geva, and Yarin Gal. 2025. Open problems in machine unlearning for AI safety.

Loic Barrault, Ondrej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussa, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Tom Kocmi, Andre Martins, Makoto Morishita, et al., editors. 2021. Proceedings of the sixth conference on machine translation. Association for Computational Linguistics, Online.

Jasmijn Bastings, Sebastian Ebert, Polina Zablotskaia, Anders Sandholm, and Katja Filippova. 2022. “Will you find these shortcuts?” A protocol for evaluating the faithfulness of input salience methods for text classification. In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang, editors, Proceedings of the 2022 conference on empirical methods in natural language processing, pages 976–991, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.

Jasmijn Bastings and Katja Filippova. 2020. The elephant in the interpretability room: Why use attention as explanation when we have saliency methods? In Afra Alishahi, Yonatan Belinkov, Grzegorz Chrupała, Dieuwke Hupkes, Yuval Pinter, and Hassan Sajjad, editors, Proceedings of the third BlackboxNLP workshop on analyzing and interpreting neural networks for NLP, pages 149–155, Online. Association for Computational Linguistics.

Rachel Bawden and Benoît Sagot. 2023. RoCS-MT: Robustness challenge set for machine translation. In Philipp Koehn, Barry Haddow, Tom Kocmi, and Christof Monz, editors, Proceedings of the eighth conference on machine translation, pages 198–216, Singapore. Association for Computational Linguistics.

Rachel Bawden, Rico Sennrich, Alexandra Birch, and Barry Haddow. 2018. Evaluating discourse phenomena in neural machine translation. In Marilyn Walker, Heng Ji, and Amanda Stent, editors, Proceedings of the 2018 conference of the north American chapter of the association for computational linguistics: Human language technologies, volume 1 (long papers), pages 1304–1313, New Orleans, Louisiana. Association for Computational Linguistics.

Yonatan Belinkov. 2022. Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics, 48(1):207–219.

Yonatan Belinkov, Nadir Durrani, Fahim Dalvi, Hassan Sajjad, and James Glass. 2017. What do neural machine translation models learn about morphology? In Regina Barzilay and Min-Yen Kan, editors, Proceedings of the 55th annual meeting of the association for computational linguistics (volume 1: Long papers), pages 861–872, Vancouver, Canada. Association for Computational Linguistics.

Yonatan Belinkov and James Glass. 2019. Analysis methods in neural language processing: A survey. Transactions of the Association for Computational Linguistics, 7:49–72.

Yonatan Belinkov, Aaron Mueller, Najoung Kim, Hosein Mohebbi, Hanjie Chen, Dana Arad, and Gabriele Sarti, editors. 2025. Proceedings of the 8th BlackboxNLP workshop: Analyzing and interpreting neural networks for NLP. Association for Computational Linguistics, Suzhou, China.

Nora Belrose, Zach Furman, Logan Smith, Danny Halawi, Igor Ostrovsky, Lev McKinney, Stella Biderman, and Jacob Steinhardt. 2023. Eliciting latent predictions from transformers with the tuned lens. ArXiv, abs/2303.08112.

Luisa Bentivogli, Arianna Bisazza, Mauro Cettolo, and Marcello Federico. 2016. Neural versus phrase-based machine translation quality: A case study. In Jian Su, Kevin Duh, and Xavier Carreras, editors, Proceedings of the 2016 conference on empirical methods in natural language processing, pages 257–267, Austin, Texas. Association for Computational Linguistics.

Nathaniel Berger, Stefan Riezler, Miriam Exel, and Matthias Huck. 2024. Post-edits are preferences too. In Barry Haddow, Tom Kocmi, Philipp Koehn, and Christof Monz, editors, Proceedings of the ninth conference on machine translation, pages 1289–1300, Miami, Florida, USA. Association for Computational Linguistics.

Federico Bianchi, Giuseppe Attanasio, Raphael Pisoni, Silvia Terragni, Gabriele Sarti, and Dario Balestri. 2023. Contrastive language–image pre-training for the Italian language. In Federico Boschetti, Gianluca E. Lebani, Bernardo Magnini, and Nicole Novielli, editors, Proceedings of the 9th italian conference on computational linguistics (CLiC-it 2023), pages 78–85, Venice, Italy. CEUR Workshop Proceedings.

BigScience Workshop, Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Luccioni, François Yvon, et al. 2022. BLOOM: A 176B-parameter open-access multilingual language model. Arxiv.

Blair Bilodeau, Natasha Jaques, Pang Wei Koh, and Been Kim. 2024. Impossibility theorems for feature attribution. Proceedings of the National Academy of Sciences, 121(2):e2304406120.

Alexandra Birch, Miles Osborne, and Philipp Koehn. 2008. Predicting success in machine translation. In Mirella Lapata and Hwee Tou Ng, editors, Proceedings of the 2008 conference on empirical methods in natural language processing, pages 745–754, Honolulu, Hawaii. Association for Computational Linguistics.

Arianna Bisazza, Ahmet Üstün, and Stephan Sportel. 2021. On the difficulty of translating free-order case-marking languages. Transactions of the Association for Computational Linguistics, 9:1233–1248.

Sidney Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, Usvsn Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, and Samuel Weinbach. 2022. GPT-NeoX-20B: An open-source autoregressive language model. In Angela Fan, Suzana Ilic, Thomas Wolf, and Matthias Gallé, editors, Proceedings of BigScience episode #5 – workshop on challenges & perspectives in creating large language models, pages 95–136, virtual+Dublin. Association for Computational Linguistics.

Frederic Blain, Chrysoula Zerva, Ricardo Rei, Nuno M. Guerreiro, Diptesh Kanojia, José G. C. de Souza, Beatriz Silva, Tânia Vaz, Yan Jingxuan, Fatemeh Azadi, Constantin Orasan, and André Martins. 2023. Findings of the WMT 2023 shared task on quality estimation. In Philipp Koehn, Barry Haddow, Tom Kocmi, and Christof Monz, editors, Proceedings of the eighth conference on machine translation, pages 629–653, Singapore. Association for Computational Linguistics.

John Blatz, Erin Fitzgerald, George Foster, Simona Gandrabur, Cyril Goutte, Alex Kulesza, Alberto Sanchis, and Nicola Ueffing. 2004. Confidence estimation for machine translation. In COLING 2004: Proceedings of the 20th international conference on computational linguistics, pages 315–321, Geneva, Switzerland. COLING.

Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach. 2020. Language (technology) is power: A critical survey of “bias” in NLP. In Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel Tetreault, editors, Proceedings of the 58th annual meeting of the association for computational linguistics, pages 5454–5476, Online. Association for Computational Linguistics.

Bernd Bohnet, Vinh Q. Tran, Pat Verga, Roee Aharoni, Daniel Andor, Livio Baldini Soares, Jacob Eisenstein, Kuzman Ganchev, Jonathan Herzig, Kai Hui, Tom Kwiatkowski, Ji Ma, Jianmo Ni, Tal Schuster, William W. Cohen, Michael Collins, Dipanjan Das, Donald Metzler, Slav Petrov, et al. 2022. Attributed question answering: Evaluation and modeling for attributed large language models. ArXiv.

Ondřej Bojar, Christian Buck, Chris Callison-Burch, Christian Federmann, Barry Haddow, Philipp Koehn, Christof Monz, Matt Post, Radu Soricut, and Lucia Specia. 2013. Findings of the 2013 Workshop on Statistical Machine Translation. In Ondrej Bojar, Christian Buck, Chris Callison-Burch, Barry Haddow, Philipp Koehn, Christof Monz, Matt Post, Herve Saint-Amand, Radu Soricut, and Lucia Specia, editors, Proceedings of the eighth workshop on statistical machine translation, pages 1–44, Sofia, Bulgaria. Association for Computational Linguistics.

Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Shujian Huang, Matthias Huck, Philipp Koehn, Qun Liu, Varvara Logacheva, Christof Monz, Matteo Negri, Matt Post, Raphael Rubino, Lucia Specia, and Marco Turchi. 2017. Findings of the 2017 conference on machine translation (WMT17). In Ondřej Bojar, Christian Buck, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, and Julia Kreutzer, editors, Proceedings of the second conference on machine translation, pages 169–214, Copenhagen, Denmark. Association for Computational Linguistics.

Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego De Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saffron Huang, Loren Maggiore, Chris Jones, Albin Cassirer, et al. 2022. Improving language models by retrieving from trillions of tokens. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato, editors, Proceedings of the 39th international conference on machine learning, volume 162, pages 2206–2240. PMLR.

Alexander Borzunov, Dmitry Baranchuk, Tim Dettmers, Maksim Riabinin, Younes Belkada, Artem Chumachenko, Pavel Samygin, and Colin Raffel. 2023. Petals: Collaborative inference and fine-tuning of large models. In Danushka Bollegala, Ruihong Huang, and Alan Ritter, editors, Proceedings of the 61st annual meeting of the association for computational linguistics (volume 3: System demonstrations), pages 558–568, Toronto, Canada. Association for Computational Linguistics.

Lynne Bowker. 2002. Computer-aided translation technology: A practical introduction. University of Ottawa Press.

Eleftheria Briakou, Di Lu, Ke Zhang, and Joel Tetreault. 2021. Olá, bonjour, salve! XFORMAL: A benchmark for multilingual formality style transfer. In Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, and Yichao Zhou, editors, Proceedings of the 2021 conference of the north american chapter of the association for computational linguistics: Human language technologies, pages 3199–3216, Online. Association for Computational Linguistics.

Eleftheria Briakou, Jiaming Luo, Colin Cherry, and Markus Freitag. 2024. Translating step-by-step: Decomposing the translation process for improved translation quality of long-form texts. In Barry Haddow, Tom Kocmi, Philipp Koehn, and Christof Monz, editors, Proceedings of the ninth conference on machine translation, pages 1301–1317, Miami, Florida, USA. Association for Computational Linguistics.

Trenton Bricken, Adly Templeton, Joshua Batson, Brian Chen, Adam Jermyn, Tom Conerly, Nick Turner, Cem Anil, Carson Denison, Amanda Askell, Robert Lasenby, Yifan Wu, Shauna Kravec, Nicholas Schiefer, Tim Maxwell, Nicholas Joseph, Zac Hatfield-Dodds, Alex Tamkin, Karina Nguyen, et al. 2023. Towards monosemanticity: Decomposing language models with dictionary learning. Transformer Circuits Thread.

Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, et al. 2020. Language models are few-shot learners. In Proceedings of the 34th international conference on neural information processing systems, Red Hook, NY, USA. Curran Associates Inc.

Emanuele Bugliarello, Sabrina J. Mielke, Antonios Anastasopoulos, Ryan Cotterell, and Naoaki Okazaki. 2020. It‘s easier to translate out of English than into it: Measuring neural translation difficulty by cross-mutual information. In Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel Tetreault, editors, Proceedings of the 58th annual meeting of the association for computational linguistics, pages 1640–1649, Online. Association for Computational Linguistics.

Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan. 2017. Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334):183–186.

Chris Callison-Burch, Cameron Fordyce, Philipp Koehn, Christof Monz, and Josh Schroeder. 2007. (Meta-) evaluation of machine translation. In Chris Callison-Burch, Philipp Koehn, Cameron Shaw Fordyce, and Christof Monz, editors, Proceedings of the second workshop on statistical machine translation, pages 136–158, Prague, Czech Republic. Association for Computational Linguistics.

Sara Candussio, Gaia Saveri, Gabriele Sarti, and Luca Bortolussi. 2025. Bridging logic and learning: Decoding temporal logic embeddings via transformers. In Machine learning and knowledge discovery in databases. Research track. Springer Nature Switzerland.

Stephen Casper, Carson Ezell, Charlotte Siegmann, Noam Kolt, Taylor Lynn Curtis, Benjamin Bucknall, Andreas Haupt, Kevin Wei, Jérémy Scheurer, Marius Hobbhahn, Lee Sharkey, Satyapriya Krishna, Marvin Von Hagen, Silas Alberti, Alan Chan, Qinyi Sun, Michael Gerovitch, David Bau, Max Tegmark, et al. 2024. Black-box access is insufficient for rigorous AI audits. In Proceedings of the 2024 ACM conference on fairness, accountability, and transparency, pages 2254–2272, New York, NY, USA. Association for Computing Machinery.

Sheila Castilho, Joss Moorkens, Federico Gaspari, Iacer Calixto, John Tinsley, and Andy Way. 2017. Is neural machine translation the new state of the art? The Prague Bulletin of Mathematical Linguistics, 108(1):109–120.

Mauro Cettolo, Marcello Federico, Luisa Bentivogli, Jan Niehues, Sebastian Stüker, Katsuhito Sudoh, Koichiro Yoshino, and Christian Federmann. 2017. Overview of the IWSLT 2017 evaluation campaign. In Sakriani Sakti and Masao Utiyama, editors, Proceedings of the 14th international conference on spoken language translation, pages 2–14, Tokyo, Japan. International Workshop on Spoken Language Translation.

Sviatoslav Chalnev, Matthew Siu, and Arthur Conmy. 2024. Improving steering vectors by targeting sparse autoencoder features. Arxiv.

Yangyi Chen, Lifan Yuan, Ganqu Cui, Zhiyuan Liu, and Heng Ji. 2023. A close look into the calibration of pre-trained language models. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki, editors, Proceedings of the 61st annual meeting of the association for computational linguistics (volume 1: Long papers), pages 1343–1367, Toronto, Canada. Association for Computational Linguistics.

Won Ik Cho, Ji Won Kim, Seok Min Kim, and Nam Soo Kim. 2019. On measuring gender bias in translation of gender-neutral pronouns. In Marta R. Costa-jussà, Christian Hardmeier, Will Radford, and Kellie Webster, editors, Proceedings of the first workshop on gender bias in natural language processing, pages 173–181, Florence, Italy. Association for Computational Linguistics.

Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, et al. 2023. PaLM: Scaling language modeling with pathways. Journal of Machine Learning Research, 24(240):1–113.

George Chrysostomou and Nikolaos Aletras. 2022. An empirical study on explanations in out-of-domain settings. In Smaranda Muresan, Preslav Nakov, and Aline Villavicencio, editors, Proceedings of the 60th annual meeting of the association for computational linguistics (volume 1: Long papers), pages 6920–6938, Dublin, Ireland. Association for Computational Linguistics.

Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, et al. 2024. Scaling instruction-finetuned language models. Journal of Machine Learning Research, 25(70):1–53.

Kenneth W. Church and Eduard H. Hovy. 1993. Good applications for crummy machine translation. Machine Translation, 8(4):239–258.

Cristiano Ciaccio, Gabriele Sarti, Alessio Miaschi, and Felice Dell’Orletta. 2025. Crossword space: Latent manifold learning for italian crosswords and beyond. In Cristina Bosco, Elisabetta Jezek, Marco Polignano, and Manuela Sanguinetti, editors, Proceedings of the 11th italian conference on computational linguistics (CLiC-it 2023), Cagliari, Italy. CEUR Workshop Proceedings.

Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning. 2019. What does BERT look at? An analysis of BERT‘s attention. In Tal Linzen, Grzegorz Chrupała, Yonatan Belinkov, and Dieuwke Hupkes, editors, Proceedings of the 2019 ACL workshop BlackboxNLP: Analyzing and interpreting neural networks for NLP, pages 276–286, Florence, Italy. Association for Computational Linguistics.

Benjamin Cohen-Wang, Harshay Shah, Kristian Georgiev, and Aleksander Mądry. 2024. ContextCite: Attributing model generation to context. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang, editors, Advances in neural information processing systems, volume 37, pages 95764–95807. Curran Associates, Inc.

Çağrı Çöltekin and Taraka Rama. 2023. What do complexity measures measure? Correlating and validating corpus-based measures of morphological complexity. Linguistics Vanguard, 9(s1):27–43.

Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020. Unsupervised cross-lingual representation learning at scale. In Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel Tetreault, editors, Proceedings of the 58th annual meeting of the association for computational linguistics, pages 8440–8451, Online. Association for Computational Linguistics.

Alexis Conneau and Guillaume Lample. 2019. Cross-lingual language model pretraining. In H. Wallach, H. Larochelle, A. Beygelzimer, F. dAlché-Buc, E. Fox, and R. Garnett, editors, Advances in neural information processing systems, volume 32. Curran Associates, Inc.

Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel Bowman, Holger Schwenk, and Veselin Stoyanov. 2018. XNLI: Evaluating cross-lingual sentence representations. In Ellen Riloff, David Chiang, Julia Hockenmaier, and Jun’ichi Tsujii, editors, Proceedings of the 2018 conference on empirical methods in natural language processing, pages 2475–2485, Brussels, Belgium. Association for Computational Linguistics.

Sven Coppers, Jan Van den Bergh, Kris Luyten, Karin Coninx, Iulianna Van der Lek-Ciudin, Tom Vanallemeersch, and Vincent Vandeghinste. 2018. Intellingo: An intelligible translation environment. In Proceedings of the 2018 CHI conference on human factors in computing systems, pages 1–13.

Ryan Cotterell, Sabrina J. Mielke, Jason Eisner, and Brian Roark. 2018. Are all languages equally hard to language-model? In Marilyn Walker, Heng Ji, and Amanda Stent, editors, Proceedings of the 2018 conference of the north American chapter of the association for computational linguistics: Human language technologies, volume 2 (short papers), pages 536–541, New Orleans, Louisiana. Association for Computational Linguistics.

Ian Covert, Scott Lundberg, and Su-In Lee. 2021. Explaining by removing: A unified framework for model explanation. Journal of Machine Learning Research, 22(209):1–90.

Jonathan Crabbé and Mihaela van der Schaar. 2023. Evaluating the robustness of interpretability methods through explanation invariance and equivariance. In Thirty-seventh conference on neural information processing systems.

Menglong Cui, Pengzhi Gao, Wei Liu, Jian Luan, and Bin Wang. 2025. Multilingual machine translation with open large language models at practical scale: An empirical study. In Luis Chiruzzo, Alan Ritter, and Lu Wang, editors, Proceedings of the 2025 conference of the nations of the americas chapter of the association for computational linguistics: Human language technologies (volume 1: Long papers), pages 5420–5443, Albuquerque, New Mexico. Association for Computational Linguistics.

Anna Currey, Maria Nadejde, Raghavendra Reddy Pappagari, Mia Mayer, Stanislas Lauly, Xing Niu, Benjamin Hsu, and Georgiana Dinu. 2022. MT-GenEval: A counterfactual and contextual dataset for evaluating gender accuracy in machine translation. In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang, editors, Proceedings of the 2022 conference on empirical methods in natural language processing, pages 4287–4299, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.

Joke Daems, Sonia Vandepitte, Robert J. Hartsuiker, and Lieve Macken. 2017a. Identifying the machine translation error types with the greatest impact on post-editing effort. Frontiers in Psychology, 8.

Joke Daems, Sonia Vandepitte, Robert Hartsuiker, and Lieve Macken. 2017b. Translation methods and experience: A comparative analysis of human translation and post-editing with students and professional translators. Meta : journal des traducteurs / Meta: Translators’ Journal, 62(2):245–270.

Damai Dai, Li Dong, Yaru Hao, Zhifang Sui, Baobao Chang, and Furu Wei. 2022. Knowledge neurons in pretrained transformers. In Smaranda Muresan, Preslav Nakov, and Aline Villavicencio, editors, Proceedings of the 60th annual meeting of the association for computational linguistics (volume 1: Long papers), pages 8493–8502, Dublin, Ireland. Association for Computational Linguistics.

David Dale, Elena Voita, Loic Barrault, and Marta R. Costa-jussà. 2023a. Detecting and mitigating hallucinations in machine translation: Model internal workings alone do well, sentence similarity Even better. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki, editors, Proceedings of the 61st annual meeting of the association for computational linguistics (volume 1: Long papers), pages 36–50, Toronto, Canada. Association for Computational Linguistics.

David Dale, Elena Voita, Janice Lam, Prangthip Hansanti, Christophe Ropers, Elahe Kalbassi, Cynthia Gao, Loic Barrault, and Marta Costa-jussà. 2023b. HalOmi: A manually annotated benchmark for multilingual hallucination and omission detection in machine translation. In Houda Bouamor, Juan Pino, and Kalika Bali, editors, Proceedings of the 2023 conference on empirical methods in natural language processing, pages 638–653, Singapore. Association for Computational Linguistics.

Xuan-Quy Dao and Ngoc-Bich Le. 2023. Chatgpt is good but bing chat is better for vietnamese students. Arxiv.

Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer. 2022. GPT3.int8(): 8-bit matrix multiplication for transformers at scale. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Advances in neural information processing systems, volume 35, pages 30318–30332. Curran Associates, Inc.

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Jill Burstein, Christy Doran, and Thamar Solorio, editors, Proceedings of the 2019 conference of the north American chapter of the association for computational linguistics: Human language technologies, volume 1 (long and short papers), pages 4171–4186, Minneapolis, Minnesota. Association for Computational Linguistics.

Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, and Byron C. Wallace. 2020. ERASER: A benchmark to evaluate rationalized NLP models. In Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel Tetreault, editors, Proceedings of the 58th annual meeting of the association for computational linguistics, pages 4443–4458, Online. Association for Computational Linguistics.

Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Baobao Chang, Xu Sun, Lei Li, and Zhifang Sui. 2024. A survey on in-context learning. In Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen, editors, Proceedings of the 2024 conference on empirical methods in natural language processing, pages 1107–1128, Miami, Florida, USA. Association for Computational Linguistics.

David L. Donoho and Michael Elad. 2003. Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ¹ minimization. Proceedings of the National Academy of Sciences, 100(5):2197–2202.

Finale Doshi-Velez and Been Kim. 2017. Towards a rigorous science of interpretable machine learning.

Zi-Yi Dou and Graham Neubig. 2021. Word alignment by fine-tuning embeddings on parallel corpora. In Paola Merlo, Jorg Tiedemann, and Reut Tsarfaty, editors, Proceedings of the 16th conference of the european chapter of the association for computational linguistics: Main volume, pages 2112–2128, Online. Association for Computational Linguistics.

Esin Durmus, He He, and Mona Diab. 2020. FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization. In Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel Tetreault, editors, Proceedings of the 58th annual meeting of the association for computational linguistics, pages 5055–5070, Online. Association for Computational Linguistics.

Lukas Edman, Gabriele Sarti, Antonio Toral, Gertjan van Noord, and Arianna Bisazza. 2024. Are character-level translations worth the wait? Comparing ByT5 and mT5 for machine translation. Transactions of the Association for Computational Linguistics, 12:392–410.

Upol Ehsan, Q. Vera Liao, Michael Muller, Mark O. Riedl, and Justin D. Weisz. 2021. Expanding explainability: Towards social transparency in AI systems. In Proceedings of the 2021 CHI conference on human factors in computing systems, New York, NY, USA. Association for Computing Machinery.

Upol Ehsan, Samir Passi, Q. Vera Liao, Larry Chan, I-Hsiang Lee, Michael Muller, and Mark O Riedl. 2024. The who in XAI: How AI background shapes perceptions of AI explanations. In Proceedings of the 2024 CHI conference on human factors in computing systems, New York, NY, USA. Association for Computing Machinery.

Bryan Eikema and Wilker Aziz. 2020. Is MAP decoding all you need? The inadequacy of the mode in neural machine translation. In Donia Scott, Nuria Bel, and Chengqing Zong, editors, Proceedings of the 28th international conference on computational linguistics, pages 4506–4520, Barcelona, Spain (Online). International Committee on Computational Linguistics.

Nelson Elhage, Tristan Hume, Catherine Olsson, Nicholas Schiefer, Tom Henighan, Shauna Kravec, Zac Hatfield-Dodds, Robert Lasenby, Dawn Drain, Carol Chen, Roger Grosse, Sam McCandlish, Jared Kaplan, Dario Amodei, Martin Wattenberg, and Christopher Olah. 2022. Toy models of superposition. Transformer Circuits Thread.

Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, et al. 2021. A mathematical framework for transformer circuits. Transformer Circuits Thread. https://transformer-circuits.pub/2021/framework/index.html.

Joseph Enguehard. 2023. Sequential integrated gradients: A simple but effective method for explaining language models. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki, editors, Findings of the association for computational linguistics: ACL 2023, pages 7555–7565, Toronto, Canada. Association for Computational Linguistics.

Sugyeong Eo, Chanjun Park, Hyeonseok Moon, Jaehyung Seo, and Heuiseok Lim. 2022. Word-level quality estimation for korean-english neural machine translation. IEEE Access, 10:44964–44973.

Johannes Eschbach-Dymanus, Frank Essenberger, Bianka Buschbeck, and Miriam Exel. 2024. Exploring the effectiveness of LLM domain adaptation for business IT machine translation. In Carolina Scarton, Charlotte Prescott, Chris Bayliss, Chris Oakley, Joanna Wright, Stuart Wrigley, Xingyi Song, Edward Gow-Smith, Rachel Bawden, Víctor M Sánchez-Cartagena, Patrick Cadwell, Ekaterina Lapshinova-Koltunski, Vera Cabarrão, Konstantinos Chatzitheodorou, Mary Nurminen, Diptesh Kanojia, and Helena Moniz, editors, Proceedings of the 25th annual conference of the european association for machine translation (volume 1), pages 610–622, Sheffield, UK. European Association for Machine Translation (EAMT).

Ekaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov, Sergey Petrakov, Haonan Li, Hamdy Mubarak, Evgenii Tsymbalov, Gleb Kuzmin, Alexander Panchenko, Timothy Baldwin, Preslav Nakov, and Maxim Panov. 2024. Fact-checking the output of large language models via token-level uncertainty quantification. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar, editors, Findings of the association for computational linguistics: ACL 2024, pages 9367–9385, Bangkok, Thailand. Association for Computational Linguistics.

Ekaterina Fadeeva, Roman Vashurin, Akim Tsvigun, Artem Vazhentsev, Sergey Petrakov, Kirill Fedyanin, Daniil Vasilev, Elizaveta Goncharova, Alexander Panchenko, Maxim Panov, Timothy Baldwin, and Artem Shelmanov. 2023. LM-polygraph: Uncertainty estimation for language models. In Yansong Feng and Els Lefever, editors, Proceedings of the 2023 conference on empirical methods in natural language processing: System demonstrations, pages 446–461, Singapore. Association for Computational Linguistics.

Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Çelebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, and Armand Joulin. 2021. Beyond english-centric multilingual machine translation. Journal of Machine Learning Research, 22(107):1–48.

Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, and Michael Auli. 2019. ELI5: Long form question answering. In Anna Korhonen, David Traum, and Lluís Màrquez, editors, Proceedings of the 57th annual meeting of the association for computational linguistics, pages 3558–3567, Florence, Italy. Association for Computational Linguistics.

Anna Farkas and Renáta Németh. 2022. How to measure gender bias in machine translation: Real-world oriented machine translators, multiple reference points. Social Sciences & Humanities Open, 5(1):100239.

Marcello Federico, Nicola Bertoldi, Marco Trombetti, and Alessandro Cattelan. 2014. MateCat: An open source CAT tool for MT post-editing. In Proceedings of the 11th conference of the association for machine translation in the americas: tutorials, Vancouver, Canada. Association for Machine Translation in the Americas.

Thomas Fel. 2024. Sparks of explainability: Recent advancements in explaining large vision models. PhD thesis, University of Toulouse.

Nils Feldhus, Robert Schwarzenberg, and Sebastian Möller. 2021. Thermostat: A large collection of NLP model explanations and analysis tools. In Heike Adel and Shuming Shi, editors, Proceedings of the 2021 conference on empirical methods in natural language processing: System demonstrations, pages 87–95, Online; Punta Cana, Dominican Republic. Association for Computational Linguistics.

Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, Naveen Arivazhagan, and Wei Wang. 2022. Language-agnostic BERT sentence embedding. In Smaranda Muresan, Preslav Nakov, and Aline Villavicencio, editors, Proceedings of the 60th annual meeting of the association for computational linguistics (volume 1: Long papers), pages 878–891, Dublin, Ireland. Association for Computational Linguistics.

Shi Feng, Eric Wallace, Alvin Grissom II, Mohit Iyyer, Pedro Rodriguez, and Jordan Boyd-Graber. 2018. Pathologies of neural models make interpretations difficult. In Ellen Riloff, David Chiang, Julia Hockenmaier, and Jun’ichi Tsujii, editors, Proceedings of the 2018 conference on empirical methods in natural language processing, pages 3719–3728, Brussels, Belgium. Association for Computational Linguistics.

Patrick Fernandes, Daniel Deutsch, Mara Finkelstein, Parker Riley, André Martins, Graham Neubig, Ankush Garg, Jonathan Clark, Markus Freitag, and Orhan Firat. 2023a. The devil is in the errors: Leveraging large language models for fine-grained machine translation evaluation. In Philipp Koehn, Barry Haddow, Tom Kocmi, and Christof Monz, editors, Proceedings of the eighth conference on machine translation, pages 1066–1083, Singapore. Association for Computational Linguistics.

Patrick Fernandes, António Farinhas, Ricardo Rei, José G. C. de Souza, Perez Ogayo, Graham Neubig, and Andre Martins. 2022. Quality-aware decoding for neural machine translation. In Marine Carpuat, Marie-Catherine de Marneffe, and Ivan Vladimir Meza Ruiz, editors, Proceedings of the 2022 conference of the north american chapter of the association for computational linguistics: Human language technologies, pages 1396–1412, Seattle, United States. Association for Computational Linguistics.

Patrick Fernandes, Kayo Yin, Emmy Liu, André Martins, and Graham Neubig. 2023b. When does translation require context? A data-driven, multilingual exploration. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki, editors, Proceedings of the 61st annual meeting of the association for computational linguistics (volume 1: Long papers), pages 606–626, Toronto, Canada. Association for Computational Linguistics.

Patrick Fernandes, Kayo Yin, Graham Neubig, and André F. T. Martins. 2021. Measuring and increasing context usage in context-aware machine translation. In Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli, editors, Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (volume 1: Long papers), pages 6467–6478, Online. Association for Computational Linguistics.

Javier Ferrando, Gerard I. Gállego, Belen Alastruey, Carlos Escolano, and Marta R. Costa-jussà. 2022a. Towards opening the black box of neural machine translation: Source and target interpretations of the transformer. In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang, editors, Proceedings of the 2022 conference on empirical methods in natural language processing, pages 8756–8769, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.

Javier Ferrando, Gerard I. Gállego, and Marta R. Costa-jussà. 2022b. Measuring the mixing of contextual information in the transformer. In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang, editors, Proceedings of the 2022 conference on empirical methods in natural language processing, pages 8698–8714, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.

Javier Ferrando, Gerard I. Gállego, Ioannis Tsiamas, and Marta R. Costa-jussà. 2023. Explaining how transformers use context to build predictions. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki, editors, Proceedings of the 61st annual meeting of the association for computational linguistics (volume 1: Long papers), pages 5486–5513, Toronto, Canada. Association for Computational Linguistics.

Javier Ferrando, Oscar Balcells Obeso, Senthooran Rajamanoharan, and Neel Nanda. 2025. Do i know this entity? Knowledge awareness and hallucinations in language models. In The thirteenth international conference on learning representations.

Javier Ferrando, Gabriele Sarti, Arianna Bisazza, and Marta R. Costa-jussà. 2024. A primer on the inner workings of transformer-based language models. Arxiv Preprint.

Jaden Fried Fiotto-Kaufman, Alexander Russell Loftus, Eric Todd, Jannik Brinkmann, Koyena Pal, Dmitrii Troitskii, Michael Ripa, Adam Belfki, Can Rager, Caden Juang, Aaron Mueller, Samuel Marks, Arnab Sen Sharma, Francesca Lucchetti, Nikhil Prakash, Carla E. Brodley, Arjun Guha, Jonathan Bell, Byron C Wallace, et al. 2025. NNsight and NDIF: Democratizing access to open-weight foundation model internals. In The thirteenth international conference on learning representations.

Lucie Flekova, Jordan Carpenter, Salvatore Giorgi, Lyle Ungar, and Daniel Preoţiuc-Pietro. 2016. Analyzing biases in human perception of user age and gender from text. In Katrin Erk and Noah A. Smith, editors, Proceedings of the 54th annual meeting of the association for computational linguistics (volume 1: Long papers), pages 843–854, Berlin, Germany. Association for Computational Linguistics.

Marina Fomicheva, Piyawat Lertvittayakumjorn, Wei Zhao, Steffen Eger, and Yang Gao. 2021. The Eval4NLP shared task on explainable quality estimation: Overview and results. In Yang Gao, Steffen Eger, Wei Zhao, Piyawat Lertvittayakumjorn, and Marina Fomicheva, editors, Proceedings of the 2nd workshop on evaluation and comparison of NLP systems, pages 165–178, Punta Cana, Dominican Republic. Association for Computational Linguistics.

Marina Fomicheva and Lucia Specia. 2019. Taking MT evaluation metrics to extremes: Beyond correlation with human judgments. Computational Linguistics, 45(3):515–558.

Marina Fomicheva, Lucia Specia, and Nikolaos Aletras. 2022a. Translation error detection as rationale extraction. In Smaranda Muresan, Preslav Nakov, and Aline Villavicencio, editors, Findings of the association for computational linguistics: ACL 2022, pages 4148–4159, Dublin, Ireland. Association for Computational Linguistics.

Marina Fomicheva, Shuo Sun, Erick Fonseca, Chrysoula Zerva, Frédéric Blain, Vishrav Chaudhary, Francisco Guzmán, Nina Lopatina, Lucia Specia, and André F. T. Martins. 2022b. MLQE-PE: A multilingual quality estimation and post-editing dataset. In Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, and Stelios Piperidis, editors, Proceedings of the thirteenth language resources and evaluation conference, pages 4963–4974, Marseille, France. European Language Resources Association.

Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, Frédéric Blain, Francisco Guzmán, Mark Fishel, Nikolaos Aletras, Vishrav Chaudhary, and Lucia Specia. 2020. Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics, 8:539–555.

Markus Freitag, George Foster, David Grangier, Viresh Ratnakar, Qijun Tan, and Wolfgang Macherey. 2021a. Experts, errors, and context: A large-scale study of human evaluation for machine translation. Transactions of the Association for Computational Linguistics, 9:1460–1474.

Markus Freitag, Nitika Mathur, Daniel Deutsch, Chi-Kiu Lo, Eleftherios Avramidis, Ricardo Rei, Brian Thompson, Frederic Blain, Tom Kocmi, Jiayi Wang, David Ifeoluwa Adelani, Marianna Buchicchio, Chrysoula Zerva, and Alon Lavie. 2024. Are LLMs breaking MT metrics? Results of the WMT24 metrics shared task. In Barry Haddow, Tom Kocmi, Philipp Koehn, and Christof Monz, editors, Proceedings of the ninth conference on machine translation, pages 47–81, Miami, Florida, USA. Association for Computational Linguistics.

Markus Freitag, Nitika Mathur, Chi-kiu Lo, Eleftherios Avramidis, Ricardo Rei, Brian Thompson, Tom Kocmi, Frederic Blain, Daniel Deutsch, Craig Stewart, Chrysoula Zerva, Sheila Castilho, Alon Lavie, and George Foster. 2023. Results of WMT23 metrics shared task: Metrics might be guilty but references are not innocent. In Philipp Koehn, Barry Haddow, Tom Kocmi, and Christof Monz, editors, Proceedings of the eighth conference on machine translation, pages 578–628, Singapore. Association for Computational Linguistics.

Markus Freitag, Ricardo Rei, Nitika Mathur, Chi-kiu Lo, Craig Stewart, Eleftherios Avramidis, Tom Kocmi, George Foster, Alon Lavie, and André F. T. Martins. 2022. Results of WMT22 metrics shared task: Stop using BLEU – neural metrics are better and more robust. In Philipp Koehn, Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Tom Kocmi, André Martins, Makoto Morishita, et al., editors, Proceedings of the seventh conference on machine translation (WMT), pages 46–68, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.

Markus Freitag, Ricardo Rei, Nitika Mathur, Chi-kiu Lo, Craig Stewart, George Foster, Alon Lavie, and Ondřej Bojar. 2021b. Results of the WMT21 metrics shared task: Evaluating metrics with expert-based human evaluations on TED and news domain. In Loic Barrault, Ondrej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussa, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Tom Kocmi, Andre Martins, Makoto Morishita, et al., editors, Proceedings of the sixth conference on machine translation, pages 733–774, Online. Association for Computational Linguistics.

Yarin Gal and Zoubin Ghahramani. 2016. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In Maria Florina Balcan and Kilian Q. Weinberger, editors, Proceedings of the 33rd international conference on machine learning, volume 48, pages 1050–1059, New York, NY, USA. Proceedings of Machine Learning Research (PLMR).

Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, and Connor Leahy. 2021. The pile: An 800GB dataset of diverse text for language modeling. Arxiv.

Tianyu Gao, Howard Yen, Jiatong Yu, and Danqi Chen. 2023a. Enabling large language models to generate text with citations. In Houda Bouamor, Juan Pino, and Kalika Bali, editors, Proceedings of the 2023 conference on empirical methods in natural language processing, pages 6465–6488, Singapore. Association for Computational Linguistics.

Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang. 2023b. Retrieval-augmented generation for large language models: A survey. ArXiv.

Ignacio Garcia. 2009. Beyond translation memory: Computers and the professional translator. The Journal of Specialised Translation.

Xavier Garcia, Noah Constant, Mandy Guo, and Orhan Firat. 2021. Towards universality in multilingual text rewriting. Arxiv.

Xavier Garcia and Orhan Firat. 2022. Using natural language prompts for machine translation. Arxiv.

Xiao Ge, Chunchen Xu, Daigo Misaki, Hazel Rose Markus, and Jeanne L Tsai. 2024. How culture shapes what people want from AI. In Proceedings of the 2024 CHI conference on human factors in computing systems, New York, NY, USA. Association for Computing Machinery.

Viveta Gene. 2021. The post-editing workflow: Training challenges for LSPs, post-editors and academia. In Ruslan Mitkov, Vilelmini Sosoni, Julie Christine Giguère, Elena Murgolo, and Elizabeth Deysel, editors, Proceedings of the translation and interpreting technology online conference, pages 187–198, Held Online. INCOMA Ltd.

Daniela Gerz, Ivan Vulić, Edoardo Maria Ponti, Roi Reichart, and Anna Korhonen. 2018. On the relation between linguistic typology and (limitations of) multilingual language modeling. In Ellen Riloff, David Chiang, Julia Hockenmaier, and Jun’ichi Tsujii, editors, Proceedings of the 2018 conference on empirical methods in natural language processing, pages 316–327, Brussels, Belgium. Association for Computational Linguistics.

Mor Geva, Avi Caciularu, Kevin Wang, and Yoav Goldberg. 2022. Transformer feed-forward layers build predictions by promoting concepts in the vocabulary space. In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang, editors, Proceedings of the 2022 conference on empirical methods in natural language processing, pages 30–45, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.

Mor Geva, Roei Schuster, Jonathan Berant, and Omer Levy. 2021. Transformer feed-forward layers are key-value memories. In Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih, editors, Proceedings of the 2021 conference on empirical methods in natural language processing, pages 5484–5495, Online; Punta Cana, Dominican Republic. Association for Computational Linguistics.

Mario Giulianelli, Joris Baan, Wilker Aziz, Raquel Fernández, and Barbara Plank. 2023. What comes next? Evaluating uncertainty in neural text generators against human production variability. In Houda Bouamor, Juan Pino, and Kalika Bali, editors, Proceedings of the 2023 conference on empirical methods in natural language processing, pages 14349–14371, Singapore. Association for Computational Linguistics.

Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep learning. MIT Press.

Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, and Alexis Conneau. 2021. Larger-scale transformers for multilingual masked language modeling. In Anna Rogers, Iacer Calixto, Ivan Vulić, Naomi Saphra, Nora Kassner, Oana-Maria Camburu, Trapit Bansal, and Vered Shwartz, editors, Proceedings of the 6th workshop on representation learning for NLP (RepL4NLP-2021), pages 29–33, Online. Association for Computational Linguistics.

Naman Goyal, Cynthia Gao, Vishrav Chaudhary, Peng-Jen Chen, Guillaume Wenzek, Da Ju, Sanjana Krishnan, Marc’Aurelio Ranzato, Francisco Guzmán, and Angela Fan. 2022. The Flores-101 evaluation benchmark for low-resource and multilingual machine translation. Transactions of the Association for Computational Linguistics, 10:522–538.

Tanya Goyal and Greg Durrett. 2021. Annotating and modeling fine-grained factuality in summarization. In Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, and Yichao Zhou, editors, Proceedings of the 2021 conference of the north american chapter of the association for computational linguistics: Human language technologies, pages 1449–1462, Online. Association for Computational Linguistics.

Yvette Graham, Timothy Baldwin, Alistair Moffat, and Justin Zobel. 2013. Continuous measurement scales in human evaluation of machine translation. In Antonio Pareja-Lora, Maria Liakata, and Stefanie Dipper, editors, Proceedings of the 7th linguistic annotation workshop and interoperability with discourse, pages 33–41, Sofia, Bulgaria. Association for Computational Linguistics.

Spence Green, Jeffrey Heer, and Christopher D. Manning. 2013. The efficacy of human post-editing for language translation. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 439–448, New York, NY, USA. Association for Computing Machinery.

Ana Guerberof. 2009. Productivity and quality in MT post-editing. In Beyond translation memories: New tools for translators workshop, Ottawa, Canada.

Ana Guerberof-Arenas and Joss Moorkens. 2023. Ethics and machine translation: The end user perspective. In Towards responsible machine translation: Ethical and legal considerations in machine translation, pages 113–133. Springer International Publishing, Cham.

Ana Guerberof-Arenas and Antonio Toral. 2022. Creativity in translation: Machine translation as a constraint for literary texts. Translation Spaces, 11(2):184–212.

Nuno M. Guerreiro, Pierre Colombo, Pablo Piantanida, and André Martins. 2023a. Optimal transport for unsupervised hallucination detection in neural machine translation. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki, editors, Proceedings of the 61st annual meeting of the association for computational linguistics (volume 1: Long papers), pages 13766–13784, Toronto, Canada. Association for Computational Linguistics.

Nuno M. Guerreiro, Ricardo Rei, Daan van Stigt, Luisa Coheur, Pierre Colombo, and André F. T. Martins. 2024. Xcomet: Transparent machine translation evaluation through fine-grained error detection. Transactions of the Association for Computational Linguistics, 12:979–995.

Nuno M. Guerreiro, Elena Voita, and André Martins. 2023b. Looking for a needle in a haystack: A comprehensive study of hallucinations in neural machine translation. In Andreas Vlachos and Isabelle Augenstein, editors, Proceedings of the 17th conference of the european chapter of the association for computational linguistics, pages 1059–1075, Dubrovnik, Croatia. Association for Computational Linguistics.

Abhijeet Gupta, Gemma Boleda, Marco Baroni, and Sebastian Padó. 2015. Distributional vectors encode referential attributes. In Lluís Màrquez, Chris Callison-Burch, and Jian Su, editors, Proceedings of the 2015 conference on empirical methods in natural language processing, pages 12–21, Lisbon, Portugal. Association for Computational Linguistics.

Christian Hadiwinoto. 2017. Book review: Syntax-based statistical machine translation by philip Williams, rico Sennrich, matt post and philipp Koehn. Computational Linguistics, 43(4):893–896.

Zellig S. Harris. 1954. Distributional structure. Word, 10(2-3):146–162.

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In 2016 IEEE conference on computer vision and pattern recognition (CVPR), pages 770–778, Los Alamitos, CA, USA. IEEE Computer Society.

Pengcheng He, Jianfeng Gao, and Weizhu Chen. 2023. DeBERTaV3: Improving DeBERTa using ELECTRA-style pre-training with gradient-disentangled embedding sharing. In Proceedings of the 11th international conference on learning representations.

Roee Hendel, Mor Geva, and Amir Globerson. 2023. In-context learning creates task vectors. In Houda Bouamor, Juan Pino, and Kalika Bali, editors, Findings of the association for computational linguistics: EMNLP 2023, pages 9318–9333, Singapore. Association for Computational Linguistics.

Dan Hendrycks and Kevin Gimpel. 2017. A baseline for detecting misclassified and out-of-distribution examples in neural networks. In International conference on learning representations (ICLR 2017).

Dan Hendrycks and Laura Hiscott. 2025. The misguided quest for mechanistic AI interpretability. Accessed August 4, 2025.

Nico Herbig, Tim Düwel, Santanu Pal, Kalliopi Meladaki, Mahsa Monshizadeh, Antonio Krüger, and Josef van Genabith. 2020. MMPE: A Multi-Modal Interface for Post-Editing Machine Translation. In Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel Tetreault, editors, Proceedings of the 58th annual meeting of the association for computational linguistics, pages 1691–1702, Online. Association for Computational Linguistics.

Anas Himmi, Guillaume Staerman, Marine Picot, Pierre Colombo, and Nuno M Guerreiro. 2024. Enhanced hallucination detection in neural machine translation through simple detector aggregation. In Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen, editors, Proceedings of the 2024 conference on empirical methods in natural language processing, pages 18573–18583, Miami, Florida, USA. Association for Computational Linguistics.

Sepp Hochreiter. 1998. The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int. J. Uncertain. Fuzziness Knowl.-Based Syst., 6(2):107–116.

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8):1735–1780.

Ari Holtzman, Peter West, Vered Shwartz, Yejin Choi, and Luke Zettlemoyer. 2021. Surface form competition: Why the highest probability answer isn‘t always right. In Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih, editors, Proceedings of the 2021 conference on empirical methods in natural language processing, pages 7038–7051, Online; Punta Cana, Dominican Republic. Association for Computational Linguistics.

Or Honovich, Roee Aharoni, Jonathan Herzig, Hagai Taitelbaum, Doron Kukliansy, Vered Cohen, Thomas Scialom, Idan Szpektor, Avinatan Hassidim, and Yossi Matias. 2022. TRUE: Re-evaluating factual consistency evaluation. In Marine Carpuat, Marie-Catherine de Marneffe, and Ivan Vladimir Meza Ruiz, editors, Proceedings of the 2022 conference of the north american chapter of the association for computational linguistics: Human language technologies, pages 3905–3920, Seattle, United States. Association for Computational Linguistics.

Jeremy Howard and Sebastian Ruder. 2018. Universal language model fine-tuning for text classification. In Iryna Gurevych and Yusuke Miyao, editors, Proceedings of the 56th annual meeting of the association for computational linguistics (volume 1: Long papers), pages 328–339, Melbourne, Australia. Association for Computational Linguistics.

Jing Huang, Atticus Geiger, Karel D’Oosterlinck, Zhengxuan Wu, and Christopher Potts. 2023. Rigorously assessing natural language explanations of neurons. In Yonatan Belinkov, Sophie Hao, Jaap Jumelet, Najoung Kim, Arya McCarthy, and Hosein Mohebbi, editors, Proceedings of the 6th BlackboxNLP workshop: Analyzing and interpreting neural networks for NLP, pages 317–331, Singapore. Association for Computational Linguistics.

Lianzhe Huang, Shuming Ma, Dongdong Zhang, Furu Wei, and Houfeng Wang. 2022. Zero-shot cross-lingual transfer of prompt-based tuning with a unified multilingual prompt. In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang, editors, Proceedings of the 2022 conference on empirical methods in natural language processing, pages 11488–11497, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.

Robert Huben, Hoagy Cunningham, Logan Riggs Smith, Aidan Ewart, and Lee Sharkey. 2024. Sparse autoencoders find highly interpretable features in language models. In The twelfth international conference on learning representations.

William J. Hutchins. 2001. Machine translation over fifty years. Histoire Épistémologie Langage, 23:7–31.

Khondoker Ittehadul Islam and Gabriele Sarti. 2025. Reveal-bangla: A dataset for cross-lingual multi-step reasoning evaluation. Arxiv Preprint.

Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave. 2023. Atlas: Few-shot learning with retrieval augmented language models. Journal of Machine Learning Research, 24(251):1–43.

Alon Jacovi and Yoav Goldberg. 2020. Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness? In Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel Tetreault, editors, Proceedings of the 58th annual meeting of the association for computational linguistics, pages 4198–4205, Online. Association for Computational Linguistics.

Sarthak Jain and Byron C. Wallace. 2019. Attention is not Explanation. In Jill Burstein, Christy Doran, and Thamar Solorio, editors, Proceedings of the 2019 conference of the north American chapter of the association for computational linguistics: Human language technologies, volume 1 (long and short papers), pages 3543–3556, Minneapolis, Minnesota. Association for Computational Linguistics.

Stanisław Jastrzebski, Devansh Arpit, Nicolas Ballas, Vikas Verma, Tong Che, and Yoshua Bengio. 2018. Residual connections encourage iterative inference. In International conference on learning representations.

Fran Jelenić, Josip Jukić, Martin Tutek, Mate Puljiz, and Jan Snajder. 2024. Out-of-distribution detection by leveraging between-layer transformation smoothness. In The twelfth international conference on learning representations.

Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2023. Mistral 7B.

Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig. 2020. How can we know what language models know? Transactions of the Association for Computational Linguistics, 8:423–438.

Di Jin, Zhijing Jin, Zhiting Hu, Olga Vechtomova, and Rada Mihalcea. 2022. Deep learning for text style transfer: A survey. Computational Linguistics, 48(1):155–205.

Linghao Jin, Jacqueline He, Jonathan May, and Xuezhe Ma. 2023. Challenges in context-aware neural machine translation. In Houda Bouamor, Juan Pino, and Kalika Bali, editors, Proceedings of the 2023 conference on empirical methods in natural language processing, pages 15246–15263, Singapore. Association for Computational Linguistics.

Nal Kalchbrenner and Phil Blunsom. 2013. Recurrent continuous translation models. In David Yarowsky, Timothy Baldwin, Anna Korhonen, Karen Livescu, and Steven Bethard, editors, Proceedings of the 2013 conference on empirical methods in natural language processing, pages 1700–1709, Seattle, Washington, USA. Association for Computational Linguistics.

Jared Kaplan, Sam McCandlish, T. J. Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeff Wu, and Dario Amodei. 2020. Scaling laws for neural language models. ArXiv.

Sariya Karimova, Patrick Simianer, and Stefan Riezler. 2018. A user-study on online adaptation of neural machine translation to human post-edits. Machine Translation, 32(4):309–324.

Marzena Karpinska and Mohit Iyyer. 2023. Large language models effectively leverage document-level context for literary translation, but critical errors persist. In Philipp Koehn, Barry Haddow, Tom Kocmi, and Christof Monz, editors, Proceedings of the eighth conference on machine translation, pages 419–451, Singapore. Association for Computational Linguistics.

Fabio Kepler, Jonay Trénous, Marcos Treviso, Miguel Vera, and André F. T. Martins. 2019. OpenKiwi: An open source framework for quality estimation. In Marta R. Costa-jussà and Enrique Alfonseca, editors, Proceedings of the 57th annual meeting of the association for computational linguistics: System demonstrations, pages 117–122, Florence, Italy. Association for Computational Linguistics.

Yunsu Kim, Petre Petrov, Pavel Petrushkov, Shahram Khadivi, and Hermann Ney. 2019a. Pivot-based transfer learning for neural machine translation between non-English languages. In Kentaro Inui, Jing Jiang, Vincent Ng, and Xiaojun Wan, editors, Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pages 866–876, Hong Kong, China. Association for Computational Linguistics.

Yunsu Kim, Duc Thanh Tran, and Hermann Ney. 2019b. When and why is document-level context useful in neural machine translation? In Andrei Popescu-Belis, Sharid Loáiciga, Christian Hardmeier, and Deyi Xiong, editors, Proceedings of the fourth workshop on discourse in machine translation (DiscoMT 2019), pages 24–34, Hong Kong, China. Association for Computational Linguistics.

Armen Der Kiureghian and Ove Ditlevsen. 2009. Aleatory or epistemic? Does it matter? Structural Safety, 31(2):105–112. Risk Acceptance and Risk Communication.

Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi, and Kentaro Inui. 2020. Attention is not only a weight: Analyzing transformers with vector norms. In Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu, editors, Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), pages 7057–7075, Online. Association for Computational Linguistics.

Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi, and Kentaro Inui. 2021. Incorporating Residual and Normalization Layers into Analysis of Masked Language Models. In Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih, editors, Proceedings of the 2021 conference on empirical methods in natural language processing, pages 4547–4568, Online; Punta Cana, Dominican Republic. Association for Computational Linguistics.

Tom Kocmi, Eleftherios Avramidis, Rachel Bawden, Ondřej Bojar, Anton Dvorkovich, Christian Federmann, Mark Fishel, Markus Freitag, Thamme Gowda, Roman Grundkiewicz, Barry Haddow, Marzena Karpinska, Philipp Koehn, Benjamin Marie, Christof Monz, Kenton Murray, Masaaki Nagata, Martin Popel, Maja Popović, et al. 2024a. Findings of the WMT24 general machine translation shared task: The LLM era is here but MT is not solved yet. In Barry Haddow, Tom Kocmi, Philipp Koehn, and Christof Monz, editors, Proceedings of the ninth conference on machine translation, pages 1–46, Miami, Florida, USA. Association for Computational Linguistics.

Tom Kocmi, Eleftherios Avramidis, Rachel Bawden, Ondřej Bojar, Anton Dvorkovich, Christian Federmann, Mark Fishel, Markus Freitag, Thamme Gowda, Roman Grundkiewicz, Barry Haddow, Philipp Koehn, Benjamin Marie, Christof Monz, Makoto Morishita, Kenton Murray, Masaaki Nagata, Toshiaki Nakazawa, Martin Popel, et al. 2023. Findings of the 2023 conference on machine translation (WMT23): LLMs are here but not quite there yet. In Philipp Koehn, Barry Haddow, Tom Kocmi, and Christof Monz, editors, Proceedings of the eighth conference on machine translation, pages 1–42, Singapore. Association for Computational Linguistics.

Tom Kocmi and Christian Federmann. 2023a. GEMBA-MQM: Detecting translation quality error spans with GPT-4. In Philipp Koehn, Barry Haddow, Tom Kocmi, and Christof Monz, editors, Proceedings of the eighth conference on machine translation, pages 768–775, Singapore. Association for Computational Linguistics.

Tom Kocmi and Christian Federmann. 2023b. Large language models are state-of-the-art evaluators of translation quality. In Mary Nurminen, Judith Brenner, Maarit Koponen, Sirkku Latomaa, Mikhail Mikhailov, Frederike Schierl, Tharindu Ranasinghe, Eva Vanmassenhove, Sergi Alvarez Vidal, Nora Aranberri, Mara Nunziatini, Carla Parra Escartín, Mikel Forcada, Maja Popovic, Carolina Scarton, and Helena Moniz, editors, Proceedings of the 24th annual conference of the european association for machine translation, pages 193–203, Tampere, Finland. European Association for Machine Translation.

Tom Kocmi, Vilém Zouhar, Eleftherios Avramidis, Roman Grundkiewicz, Marzena Karpinska, Maja Popović, Mrinmaya Sachan, and Mariya Shmatova. 2024b. Error span annotation: A balanced approach for human evaluation of machine translation. In Barry Haddow, Tom Kocmi, Philipp Koehn, and Christof Monz, editors, Proceedings of the ninth conference on machine translation, pages 1440–1453, Miami, Florida, USA. Association for Computational Linguistics.

Philipp Koehn. 2005. Europarl: A parallel corpus for statistical machine translation. In Proceedings of machine translation summit x: papers, pages 79–86, Phuket, Thailand.

Philipp Koehn, Franz J. Och, and Daniel Marcu. 2003. Statistical phrase-based translation. In Proceedings of the 2003 human language technology conference of the north American chapter of the association for computational linguistics, pages 127–133.

Arne Köhn. 2015. What‘s in an embedding? Analyzing word embeddings through multilingual evaluation. In Lluís Màrquez, Chris Callison-Burch, and Jian Su, editors, Proceedings of the 2015 conference on empirical methods in natural language processing, pages 2067–2073, Lisbon, Portugal. Association for Computational Linguistics.

Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, and Orion Reblitz-Richardson. 2020. Captum: A unified and generic model interpretability library for PyTorch. ArXiv.

Maarit Koponen, Wilker Aziz, Luciana Ramos, and Lucia Specia. 2012. Post-editing time as a measure of cognitive effort. In Workshop on post-editing technology and practice.

Maarit Koponen, Umut Sulubacak, Kaisa Vitikainen, and Jörg Tiedemann. 2020. MT for subtitling: User evaluation of post-editing productivity. In André Martins, Helena Moniz, Sara Fumega, Bruno Martins, Fernando Batista, Luisa Coheur, Carla Parra, Isabel Trancoso, Marco Turchi, Arianna Bisazza, Joss Moorkens, Ana Guerberof, Mary Nurminen, Lena Marg, and Mikel L. Forcada, editors, Proceedings of the 22nd annual conference of the european association for machine translation, pages 115–124, Lisboa, Portugal. European Association for Machine Translation.

Hans P. Krings. 2001. Repairing texts: Empirical investigations of machine translation post-editing processes. Kent State University Press.

Kalpesh Krishna, Deepak Nathani, Xavier Garcia, Bidisha Samanta, and Partha Talukdar. 2022. Few-shot controllable style transfer for low-resource multilingual settings. In Smaranda Muresan, Preslav Nakov, and Aline Villavicencio, editors, Proceedings of the 60th annual meeting of the association for computational linguistics (volume 1: Long papers), pages 7439–7468, Dublin, Ireland. Association for Computational Linguistics.

Kalpesh Krishna, Aurko Roy, and Mohit Iyyer. 2021. Hurdles to progress in long-form question answering. In Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, and Yichao Zhou, editors, Proceedings of the 2021 conference of the north american chapter of the association for computational linguistics: Human language technologies, pages 4940–4957, Online. Association for Computational Linguistics.

Satyapriya Krishna, Tessa Han, Alex Gu, Steven Wu, Shahin Jabbari, and Himabindu Lakkaraju. 2024. The disagreement problem in explainable machine learning: A practitioner’s perspective. Transactions on Machine Learning Research.

Wojciech Kryscinski, Bryan McCann, Caiming Xiong, and Richard Socher. 2020. Evaluating the factual consistency of abstractive text summarization. In Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu, editors, Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), pages 9332–9346, Online. Association for Computational Linguistics.

Solomon Kullback and Richard A Leibler. 1951. On information and sufficiency. The annals of mathematical statistics, 22(1):79–86.

Isabel Lacruz, Michael Denkowski, and Alon Lavie. 2014. Cognitive demand and cognitive effort in post-editing. In Sharon O’Brien, Michel Simard, and Lucia Specia, editors, Proceedings of the 11th conference of the association for machine translation in the americas, pages 73–84, Vancouver, Canada. Association for Machine Translation in the Americas.

Isaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Sam Gershman, and Finale Doshi-Velez. 2019. An evaluation of the human-interpretability of explanation. ArXiv, abs/1902.00006.

Huiyuan Lai, Jiali Mao, Antonio Toral, and Malvina Nissim. 2022. Human judgement as a compass to navigate automatic metrics for formality transfer. In Anya Belz, Maja Popović, Ehud Reiter, and Anastasia Shimorina, editors, Proceedings of the 2nd workshop on human evaluation of NLP systems (HumEval), pages 102–115, Dublin, Ireland. Association for Computational Linguistics.

Surafel Melaku Lakew, Mattia Di Gangi, and Marcello Federico. 2019. Controlling the output length of neural machine translation. In Jan Niehues, Rolando Cattoni, Sebastian Stüker, Matteo Negri, Marco Turchi, Thanh-Le Ha, Elizabeth Salesky, Ramon Sanabria, Loic Barrault, Lucia Specia, and Marcello Federico, editors, Proceedings of the 16th international conference on spoken language translation, Hong Kong. Association for Computational Linguistics.

Anna Langedijk, Hosein Mohebbi, Gabriele Sarti, Willem Zuidema, and Jaap Jumelet. 2024. DecoderLens: Layerwise interpretation of encoder-decoder transformers. In Kevin Duh, Helena Gomez, and Steven Bethard, editors, Findings of the association for computational linguistics: NAACL 2024, pages 4764–4780, Mexico City, Mexico. Association for Computational Linguistics.

Samuel Läubli, Chantal Amrhein, Patrick Düggelin, Beatriz Gonzalez, Alena Zwahlen, and Martin Volk. 2019. Post-editing productivity with neural machine translation: An empirical assessment of speed and quality in the banking and finance domain. In Mikel Forcada, Andy Way, Barry Haddow, and Rico Sennrich, editors, Proceedings of machine translation summit XVII: Research track, pages 267–272, Dublin, Ireland. European Association for Machine Translation.

Samuel Läubli, Mark Fishel, Gary Massey, Maureen Ehrensberger-Dow, and Martin Volk. 2013. Assessing post-editing efficiency in a realistic translation environment. In Sharon O’Brien, Michel Simard, and Lucia Specia, editors, Proceedings of the 2nd workshop on post-editing technology and practice, Nice, France.

Samuel Läubli, Rico Sennrich, and Martin Volk. 2018. Has machine translation achieved human parity? A case for document-level evaluation. In Ellen Riloff, David Chiang, Julia Hockenmaier, and Jun’ichi Tsujii, editors, Proceedings of the 2018 conference on empirical methods in natural language processing, pages 4791–4796, Brussels, Belgium. Association for Computational Linguistics.

Jihyeon Lee, Taehee Kim, Yunwon Tae, Cheonbok Park, and Jaegul Choo. 2023a. PePe: Personalized post-editing model utilizing user-generated post-edits. In Andreas Vlachos and Isabelle Augenstein, editors, Findings of the association for computational linguistics: EACL 2023, pages 239–253, Dubrovnik, Croatia. Association for Computational Linguistics.

Seungjun Lee, Jungseob Lee, Hyeonseok Moon, Chanjun Park, Jaehyung Seo, Sugyeong Eo, Seonmin Koo, and Heuiseok Lim. 2023b. A survey on evaluation metrics for machine translation. Mathematics, 11(4).

Christoph Leiter, Piyawat Lertvittayakumjorn, Marina Fomicheva, Wei Zhao, Yang Gao, and Steffen Eger. 2024. Towards explainable evaluation metrics for machine translation. Journal of Machine Learning Research, 25(75):1–49.

Shahar Levy, Koren Lazar, and Gabriel Stanovsky. 2021. Collecting a large-scale gender bias dataset for coreference resolution and machine translation. In Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih, editors, Findings of the association for computational linguistics: EMNLP 2021, pages 2470–2480, Punta Cana, Dominican Republic. Association for Computational Linguistics.

Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020. Retrieval-augmented generation for knowledge-intensive NLP tasks. In Proceedings of the 34th international conference on neural information processing systems, Red Hook, NY, USA. Curran Associates Inc.

Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite, Abhishek Thakur, Patrick von Platen, Suraj Patil, Julien Chaumond, Mariama Drame, Julien Plu, Lewis Tunstall, Joe Davison, Mario Šaško, Gunjan Chhablani, Bhavitvya Malik, Simon Brandeis, Teven Le Scao, Victor Sanh, Canwen Xu, Nicolas Patry, et al. 2021. Datasets: A community library for natural language processing. In Heike Adel and Shuming Shi, editors, Proceedings of the 2021 conference on empirical methods in natural language processing: System demonstrations, pages 175–184, Online; Punta Cana, Dominican Republic. Association for Computational Linguistics.

Haijun Li, Tianqi Shi, Zifu Shang, Yuxuan Han, Xueyu Zhao, Hao Wang, Yu Qian, Zhiqiang Qian, Linlong Xu, Minghao Wu, Chenyang Lyu, Longyue Wang, Gongbo Tang, Weihua Luo, Zhao Xu, and Kaifu Zhang. 2025. TransBench: Benchmarking machine translation for industrial-scale applications. Arxiv.

Jiwei Li, Xinlei Chen, Eduard Hovy, and Dan Jurafsky. 2016. Visualizing and understanding neural models in NLP. In Kevin Knight, Ani Nenkova, and Owen Rambow, editors, Proceedings of the 2016 conference of the north American chapter of the association for computational linguistics: Human language technologies, pages 681–691, San Diego, California. Association for Computational Linguistics.

Xuhong Li, Haoyi Xiong, Xingjian Li, Xuanyu Wu, Xiao Zhang, Ji Liu, Jiang Bian, and Dejing Dou. 2022. Interpretable deep learning: Interpretation, interpretability, trustworthiness, and beyond. Knowledge and Information Systems, 64(12):3197–3234.

Daniel Licht, Cynthia Gao, Janice Lam, Francisco Guzman, Mona Diab, and Philipp Koehn. 2022. Consistent human evaluation of machine translation across language pairs. In Kevin Duh and Francisco Guzmán, editors, Proceedings of the 15th biennial conference of the association for machine translation in the americas (volume 1: Research track), pages 309–321, Orlando, USA. Association for Machine Translation in the Americas.

Tom Lieberum, Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Nicolas Sonnerat, Vikrant Varma, Janos Kramar, Anca Dragan, Rohin Shah, and Neel Nanda. 2024. Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2. In Yonatan Belinkov, Najoung Kim, Jaap Jumelet, Hosein Mohebbi, Aaron Mueller, and Hanjie Chen, editors, Proceedings of the 7th BlackboxNLP workshop: Analyzing and interpreting neural networks for NLP, pages 278–300, Miami, Florida, US. Association for Computational Linguistics.

Zheng Wei Lim, Ekaterina Vylomova, Charles Kemp, and Trevor Cohn. 2024. Predicting human translation difficulty with neural machine translation. Transactions of the Association for Computational Linguistics, 12:1479–1496.

Huan Lin, Liang Yao, Baosong Yang, Dayiheng Liu, Haibo Zhang, Weihua Luo, Degen Huang, and Jinsong Su. 2021. Towards user-driven neural machine translation. In Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli, editors, Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (volume 1: Long papers), pages 4008–4018, Online. Association for Computational Linguistics.

Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O’Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, et al. 2022. Few-shot learning with multilingual generative language models. In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang, editors, Proceedings of the 2022 conference on empirical methods in natural language processing, pages 9019–9052, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.

Yu-Hsiang Lin, Chian-Yu Chen, Jean Lee, Zirui Li, Yuyan Zhang, Mengzhou Xia, Shruti Rijhwani, Junxian He, Zhisong Zhang, Xuezhe Ma, Antonios Anastasopoulos, Patrick Littell, and Graham Neubig. 2019. Choosing transfer languages for cross-lingual learning. In Anna Korhonen, David Traum, and Lluís Màrquez, editors, Proceedings of the 57th annual meeting of the association for computational linguistics, pages 3125–3135, Florence, Italy. Association for Computational Linguistics.

Mary J. Lindstrom and Douglas M. Bates. 1988. Newton—raphson and EM algorithms for linear mixed-effects models for repeated-measures data. Journal of the American Statistical Association, 83(404):1014–1022.

Pierre Lison, Jörg Tiedemann, and Milen Kouylekov. 2018. OpenSubtitles2018: Statistical rescoring of sentence alignments in large, noisy parallel corpora. In Nicoletta Calzolari, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Koiti Hasida, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis, and Takenobu Tokunaga, editors, Proceedings of the eleventh international conference on language resources and evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA).

Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2022. What makes good in-context examples for GPT-3? In Eneko Agirre, Marianna Apidianaki, and Ivan Vulić, editors, Proceedings of deep learning inside out (DeeLIO 2022): The 3rd workshop on knowledge extraction and integration for deep learning architectures, pages 100–114, Dublin, Ireland; Online. Association for Computational Linguistics.

Nelson Liu, Tianyi Zhang, and Percy Liang. 2023a. Evaluating verifiability in generative search engines. In Houda Bouamor, Juan Pino, and Kalika Bali, editors, Findings of the association for computational linguistics: EMNLP 2023, pages 7001–7025, Singapore. Association for Computational Linguistics.

Xiaoming Liu, Zhaohan Zhang, Yichen Wang, Hang Pu, Yu Lan, and Chao Shen. 2023b. CoCo: Coherence-enhanced machine-generated text detection under low resource with contrastive learning. In Houda Bouamor, Juan Pino, and Kalika Bali, editors, Proceedings of the 2023 conference on empirical methods in natural language processing, pages 16167–16188, Singapore. Association for Computational Linguistics.

Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, and Luke Zettlemoyer. 2020. Multilingual denoising pre-training for neural machine translation. Transactions of the Association for Computational Linguistics, 8:726–742.

Zhongtao Liu, Parker Riley, Daniel Deutsch, Alison Lui, Mengmeng Niu, Apurva Shah, and Markus Freitag. 2024. Beyond human-only: Evaluating human-machine collaboration for collecting high-quality translation data. In Barry Haddow, Tom Kocmi, Philipp Koehn, and Christof Monz, editors, Proceedings of the ninth conference on machine translation, pages 1095–1106, Miami, Florida, USA. Association for Computational Linguistics.

Zihan Liu, Genta Indra Winata, and Pascale Fung. 2021. Continual mixed-language pre-training for extremely low-resource neural machine translation. In Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli, editors, Findings of the association for computational linguistics: ACL-IJCNLP 2021, pages 2706–2718, Online. Association for Computational Linguistics.

Arle Richard Lommel, Aljoscha Burchardt, and Hans Uszkoreit. 2013. Multidimensional quality metrics: A flexible system for assessing translation quality. In Proceedings of translating and the computer 35, London, UK. Aslib.

Arle Lommel, Serge Gladkoff, Alan Melby, Sue Ellen Wright, Ingemar Strandvik, Katerina Gasova, Angelika Vaasa, Andy Benzo, Romina Marazzato Sparano, Monica Foresi, Johani Innis, Lifeng Han, and Goran Nenadic. 2024. The multi-range theory of translation quality measurement: MQM scoring models and statistical quality control. In Marianna Martindale, Janice Campbell, Konstantin Savenkov, and Shivali Goel, editors, Proceedings of the 16th conference of the association for machine translation in the americas (volume 2: presentations), pages 75–94, Chicago, USA. Association for Machine Translation in the Americas.

António Lopes, M. Amin Farajian, Rachel Bawden, Michael Zhang, and André F. T. Martins. 2020. Document-level neural MT: A systematic comparison. In André Martins, Helena Moniz, Sara Fumega, Bruno Martins, Fernando Batista, Luisa Coheur, Carla Parra, Isabel Trancoso, Marco Turchi, Arianna Bisazza, Joss Moorkens, Ana Guerberof, Mary Nurminen, Lena Marg, and Mikel L. Forcada, editors, Proceedings of the 22nd annual conference of the european association for machine translation, pages 225–234, Lisboa, Portugal. European Association for Machine Translation.

Sheng Lu, Shan Chen, Yingya Li, Danielle Bitterman, Guergana Savova, and Iryna Gurevych. 2023. Measuring pointwise 𝒱-usable information in-context-ly. In Houda Bouamor, Juan Pino, and Kalika Bali, editors, Findings of the association for computational linguistics: EMNLP 2023, pages 15739–15756, Singapore. Association for Computational Linguistics.

Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2022. Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity. In Smaranda Muresan, Preslav Nakov, and Aline Villavicencio, editors, Proceedings of the 60th annual meeting of the association for computational linguistics (volume 1: Long papers), pages 8086–8098, Dublin, Ireland. Association for Computational Linguistics.

Scott M. Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. In Proceedings of the 31st international conference on neural information processing systems, volume 30, pages 4768–4777, Long Beach, California, USA. Curran Associates Inc.

Cheng Luo, Wei Liu, Jieyu Lin, Jiajie Zou, Ming Xiang, and Nai Ding. 2022. Simple but challenging: Natural language inference models fail on simple sentences. In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang, editors, Findings of the association for computational linguistics: EMNLP 2022, pages 3449–3462, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.

Lijia Ma, Xingchen Xu, and Yong Tan. 2024. Crafting knowledge: Exploring the creative mechanisms of chat-based search engines. Arxiv.

Mohammad Reza Ghasemi Madani, Aryo Pradipta Gema, Gabriele Sarti, Yu Zhao, Pasquale Minervini, and Andrea Passerini. 2025. Noiser: Bounded input perturbations for attributing large language models. In Second conference on language modeling.

Andreas Madsen, Sarath Chandar, and Siva Reddy. 2024. Are self-explanations from large language models faithful? In Lun-Wei Ku, Andre Martins, and Vivek Srikumar, editors, Findings of the association for computational linguistics: ACL 2024, pages 295–337, Bangkok, Thailand. Association for Computational Linguistics.

Andreas Madsen, Nicholas Meade, Vaibhav Adlakha, and Siva Reddy. 2022a. Evaluating the faithfulness of importance measures in NLP by recursively masking allegedly important tokens and retraining. In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang, editors, Findings of the association for computational linguistics: EMNLP 2022, pages 1731–1751, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.

Andreas Madsen, Siva Reddy, and Sarath Chandar. 2022b. Post-hoc interpretability for neural NLP: A survey. ACM Comput. Surv., 55(8).

Suvodeep Majumder, Stanislas Lauly, Maria Nadejde, Marcello Federico, and Georgiana Dinu. 2022. A baseline revisited: Pushing the limits of multi-segment models for context-aware translation. ArXiv, abs/2210.10906.

Samuel Marks. 2025. Downstream applications as validation of interpretability. LessWrong Post.

Samuel Marks and Max Tegmark. 2024. The geometry of truth: Emergent linear structure in large language model representations of true/false datasets. In Proceedings of the 1st conference on language modeling (COLM).

Marianna Martindale and Marine Carpuat. 2018. Fluency over adequacy: A pilot study in measuring user trust in imperfect MT. In Colin Cherry and Graham Neubig, editors, Proceedings of the 13th conference of the association for machine translation in the Americas (volume 1: Research track), pages 13–25, Boston, MA. Association for Machine Translation in the Americas.

Sameen Maruf and Gholamreza Haffari. 2018. Document context neural machine translation with memory networks. In Iryna Gurevych and Yusuke Miyao, editors, Proceedings of the 56th annual meeting of the association for computational linguistics (volume 1: Long papers), pages 1275–1284, Melbourne, Australia. Association for Computational Linguistics.

Sameen Maruf, Fahimeh Saleh, and Gholamreza Haffari. 2021. A survey on document-level neural machine translation: Methods and evaluation. ACM Comput. Surv., 54(2).

Evgeny Matusov. 2019. The challenges of using neural machine translation for literature. In James Hadley, Maja Popović, Haithem Afli, and Andy Way, editors, Proceedings of the qualities of literary machine translation, pages 10–19, Dublin, Ireland. European Association for Machine Translation.

Thomas Mayer and Michael Cysouw. 2014. Creating a massively parallel Bible corpus. In Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, and Stelios Piperidis, editors, Proceedings of the ninth international conference on language resources and evaluation (LREC‘14), pages 3158–3163, Reykjavik, Iceland. European Language Resources Association (ELRA).

Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020. On faithfulness and factuality in abstractive summarization. In Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel Tetreault, editors, Proceedings of the 58th annual meeting of the association for computational linguistics, pages 1906–1919, Online. Association for Computational Linguistics.

R. Thomas McCoy, Ellie Pavlick, and Tal Linzen. 2019. Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference. In Anna Korhonen, David Traum, and Lluís Màrquez, editors, Proceedings of the 57th annual meeting of the association for computational linguistics, pages 3428–3448, Florence, Italy. Association for Computational Linguistics.

Thomas McGrath, Daniel Balsam, Myra Deng, and Eric Ho. 2024. Understanding and steering llama 3 with sparse autoencoders. Goodfire Blog.

Nikita Mehandru, Sweta Agrawal, Yimin Xiao, Ge Gao, Elaine Khoong, Marine Carpuat, and Niloufar Salehi. 2023. Physician detection of clinical harm in machine translation: Quality estimation aids in reliance and backtranslation identifies critical errors. In Houda Bouamor, Juan Pino, and Kalika Bali, editors, Proceedings of the 2023 conference on empirical methods in natural language processing, pages 11633–11647, Singapore. Association for Computational Linguistics.

Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022. Locating and editing factual associations in GPT. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Advances in neural information processing systems, volume 35, pages 17359–17372. Curran Associates, Inc.

Jacob Menick, Maja Trebacz, Vladimir Mikulik, John Aslanides, Francis Song, Martin Chadwick, Mia Glaese, Susannah Young, Lucy Campbell-Gillingham, Geoffrey Irving, et al. 2022. Teaching language models to support answers with verified quotes. Arxiv.

Alessio Miaschi, Gabriele Sarti, Dominique Brunato, Felice Dell’Orletta, and Giulia Venturi. 2022. Probing linguistic knowledge in italian neural language models across language varieties. Italian Journal of Computational Linguistics (IJCoL), 8(1):25–44.

Paul Michel and Graham Neubig. 2018. Extreme adaptation for personalized neural machine translation. In Iryna Gurevych and Yusuke Miyao, editors, Proceedings of the 56th annual meeting of the association for computational linguistics (volume 2: Short papers), pages 312–318, Melbourne, Australia. Association for Computational Linguistics.

Lesly Miculicich, Dhananjay Ram, Nikolaos Pappas, and James Henderson. 2018. Document-level neural machine translation with hierarchical attention networks. In Ellen Riloff, David Chiang, Julia Hockenmaier, and Jun’ichi Tsujii, editors, Proceedings of the 2018 conference on empirical methods in natural language processing, pages 2947–2954, Brussels, Belgium. Association for Computational Linguistics.

Sabrina J. Mielke, Ryan Cotterell, Kyle Gorman, Brian Roark, and Jason Eisner. 2019. What kind of language is hard to language-model? In Anna Korhonen, David Traum, and Lluís Màrquez, editors, Proceedings of the 57th annual meeting of the association for computational linguistics, pages 4975–4989, Florence, Italy. Association for Computational Linguistics.

Vivek Miglani, Aobo Yang, Aram Markosyan, Diego Garcia-Olano, and Narine Kokhlikyan. 2023. Using captum to explain generative language models. In Liling Tan, Dmitrijs Milajevs, Geeticka Chauhan, Jeremy Gwinnup, and Elijah Rippeth, editors, Proceedings of the 3rd workshop for natural language processing open source software (NLP-OSS 2023), pages 165–173, Singapore. Association for Computational Linguistics.

Tomas Mikolov, Wen-tau Yih, and Geoffrey Zweig. 2013. Linguistic regularities in continuous space word representations. In Lucy Vanderwende, Hal Daumé III, and Katrin Kirchhoff, editors, Proceedings of the 2013 conference of the north American chapter of the association for computational linguistics: Human language technologies, pages 746–751, Atlanta, Georgia. Association for Computational Linguistics.

Hosein Mohebbi, Willem Zuidema, Grzegorz Chrupała, and Afra Alishahi. 2023. Quantifying context mixing in transformers. In Andreas Vlachos and Isabelle Augenstein, editors, Proceedings of the 17th conference of the european chapter of the association for computational linguistics, pages 3378–3400, Dubrovnik, Croatia. Association for Computational Linguistics.

Joss Moorkens, Antonio Toral, Sheila Castilho, and Andy Way. 2018. Translators’ perceptions of literary post-editing using statistical and neural machine translation. Translation Spaces, 7(2):240–262.

John Moran, Christian Saam, and Dave Lewis. 2014. Towards desktop-based CAT tool instrumentation. In Sharon O’Brien, Michel Simard, and Lucia Specia, editors, Proceedings of the 11th conference of the association for machine translation in the americas, pages 99–112, Vancouver, Canada. Association for Machine Translation in the Americas.

Marius Mosbach, Vagrant Gautam, Tomás Vergara Browne, Dietrich Klakow, and Mor Geva. 2024. From insights to actions: The impact of interpretability and analysis research on NLP. In Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen, editors, Proceedings of the 2024 conference on empirical methods in natural language processing, pages 3078–3105, Miami, Florida, USA. Association for Computational Linguistics.

Yasmin Moslem, Rejwanul Haque, John D. Kelleher, and Andy Way. 2023. Adaptive machine translation with large language models. In Mary Nurminen, Judith Brenner, Maarit Koponen, Sirkku Latomaa, Mikhail Mikhailov, Frederike Schierl, Tharindu Ranasinghe, Eva Vanmassenhove, Sergi Alvarez Vidal, Nora Aranberri, Mara Nunziatini, Carla Parra Escartín, Mikel Forcada, Maja Popovic, Carolina Scarton, and Helena Moniz, editors, Proceedings of the 24th annual conference of the european association for machine translation, pages 227–237, Tampere, Finland. European Association for Machine Translation.

Norman Mu, Sarah Chen, Zifan Wang, Sizhe Chen, David Karamardian, Lulwa Aljeraisy, Dan Hendrycks, and David Wagner. 2023. Can LLMs follow simple rules? Arxiv.

Benjamin Muller, John Wieting, Jonathan Clark, Tom Kwiatkowski, Sebastian Ruder, Livio Soares, Roee Aharoni, Jonathan Herzig, and Xinyi Wang. 2023. Evaluating and modeling attribution for cross-lingual question answering. In Houda Bouamor, Juan Pino, and Kalika Bali, editors, Proceedings of the 2023 conference on empirical methods in natural language processing, pages 144–157, Singapore. Association for Computational Linguistics.

Mathias Müller, Annette Rios, Elena Voita, and Rico Sennrich. 2018. A large-scale test set for the evaluation of context-aware pronoun translation in neural machine translation. In Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Lucia Specia, Marco Turchi, and Karin Verspoor, editors, Proceedings of the third conference on machine translation: Research papers, pages 61–72, Brussels, Belgium. Association for Computational Linguistics.

Maria Nadejde, Anna Currey, Benjamin Hsu, Xing Niu, Marcello Federico, and Georgiana Dinu. 2022. CoCoA-MT: A dataset and benchmark for contrastive controlled MT with application to formality. In Marine Carpuat, Marie-Catherine de Marneffe, and Ivan Vladimir Meza Ruiz, editors, Findings of the association for computational linguistics: NAACL 2022, pages 616–632, Seattle, United States. Association for Computational Linguistics.

Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al. 2021. Webgpt: Browser-assisted question-answering with human feedback. Arxiv.

Neel Nanda. 2023. Attribution patching: Activation patching at industrial scale.

Mariana Neves, Cristian Grozea, Philippe Thomas, Roland Roller, Rachel Bawden, Aurélie Névéol, Steffen Castle, Vanessa Bonato, Giorgio Maria Di Nunzio, Federica Vezzani, Maika Vicente Navarro, Lana Yeganova, and Antonio Jimeno Yepes. 2024. Findings of the WMT 2024 biomedical translation shared task: Test sets on abstract level. In Barry Haddow, Tom Kocmi, Philipp Koehn, and Christof Monz, editors, Proceedings of the ninth conference on machine translation, pages 124–138, Miami, Florida, USA. Association for Computational Linguistics.

Mariana Neves, Antonio Jimeno Yepes, Aurélie Névéol, Rachel Bawden, Giorgio Maria Di Nunzio, Roland Roller, Philippe Thomas, Federica Vezzani, Maika Vicente Navarro, Lana Yeganova, Dina Wiemann, and Cristian Grozea. 2023. Findings of the WMT 2023 biomedical translation shared task: Evaluation of ChatGPT 3.5 as a comparison system. In Philipp Koehn, Barry Haddow, Tom Kocmi, and Christof Monz, editors, Proceedings of the eighth conference on machine translation, pages 43–54, Singapore. Association for Computational Linguistics.

Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2020. Adversarial NLI: A new benchmark for natural language understanding. In Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel Tetreault, editors, Proceedings of the 58th annual meeting of the association for computational linguistics, pages 4885–4901, Online. Association for Computational Linguistics.

Xing Niu and Marine Carpuat. 2020. Controlling neural machine translation formality with synthetic supervision. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05):8568–8575.

Xing Niu, Marianna Martindale, and Marine Carpuat. 2017. A study of style in machine translation: Controlling the formality of machine translation output. In Martha Palmer, Rebecca Hwa, and Sebastian Riedel, editors, Proceedings of the 2017 conference on empirical methods in natural language processing, pages 2814–2819, Copenhagen, Denmark. Association for Computational Linguistics.

NLLB Team, Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, et al. 2024. Scaling neural machine translation to 200 languages. Nature, 630(8018):841–846.

nostalgebraist. 2020. Interpreting GPT: The logit lens. AI Alignment Forum.

Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, Charles Sutton, and Augustus Odena. 2022. Show your work: Scratchpads for intermediate computation with language models. In Deep learning for code workshop.

Franz Josef Och, Christoph Tillmann, and Hermann Ney. 1999. Improved alignment models for statistical machine translation. In 1999 joint SIGDAT conference on empirical methods in natural language processing and very large corpora.

Chris Olah. 2023. Distributed representations: Composition & superposition. Transformer Circuits Thread.

Bruno A. Olshausen and David J. Field. 1997. Sparse coding with an overcomplete basis set: A strategy employed by V1? Vision Research, 37(23):3311–3325.

OpenAI. 2023. Gpt-4 technical report. Arxiv.

Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019. Fairseq: A fast, extensible toolkit for sequence modeling. In Waleed Ammar, Annie Louis, and Nasrin Mostafazadeh, editors, Proceedings of the 2019 conference of the north American chapter of the association for computational linguistics (demonstrations), pages 48–53, Minneapolis, Minnesota. Association for Computational Linguistics.

Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: A method for automatic evaluation of machine translation. In Pierre Isabelle, Eugene Charniak, and Dekang Lin, editors, Proceedings of the 40th annual meeting of the association for computational linguistics, pages 311–318, Philadelphia, Pennsylvania, USA. Association for Computational Linguistics.

Kiho Park, Yo Joong Choe, and Victor Veitch. 2023. The linear representation hypothesis and the geometry of large language models. In Causal representation learning workshop at NeurIPS 2023.

Carla Parra Escartín and Manuel Arcedillo. 2015. Machine translation evaluation made fuzzier: A study on post-editing productivity and evaluation metrics in commercial settings. In Proceedings of machine translation summit XV: papers, Miami, USA.

Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. GloVe: Global vectors for word representation. In Alessandro Moschitti, Bo Pang, and Walter Daelemans, editors, Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532–1543, Doha, Qatar. Association for Computational Linguistics.

Fabio Petroni, Patrick Lewis, Aleksandra Piktus, Tim Rocktäschel, Yuxiang Wu, Alexander H. Miller, and Sebastian Riedel. 2020. How context affects language models’ factual predictions. In Automated knowledge base construction.

Anirudh Phukan, Shwetha Somasundaram, Apoorv Saxena, Koustava Goswami, and Balaji Vasan Srinivasan. 2024. Peering into the mind of language models: An approach for attribution in contextual question answering. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar, editors, Findings of the association for computational linguistics: ACL 2024, pages 11481–11495, Bangkok, Thailand. Association for Computational Linguistics.

Charles Pierse. 2021. Transformers interpret.

Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Dmytro Okhonko, Samuel Broscheit, Gautier Izacard, Patrick Lewis, Barlas Oğuz, Edouard Grave, Wen-tau Yih, et al. 2021. The web is your oyster-knowledge-intensive NLP against a very large web corpus. Arxiv.

Barbara Plank. 2022. The “problem” of human label variation: On ground truth in data, modeling and evaluation. In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang, editors, Proceedings of the 2022 conference on empirical methods in natural language processing, pages 10671–10682, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.

Barbara Plank, Dirk Hovy, and Anders Søgaard. 2014. Linguistically debatable or just plain wrong? In Kristina Toutanova and Hua Wu, editors, Proceedings of the 52nd annual meeting of the association for computational linguistics (volume 2: Short papers), pages 507–511, Baltimore, Maryland. Association for Computational Linguistics.

Mirko Plitt and François Masselot. 2010. A Productivity Test of Statistical Machine Translation Post-Editing in a Typical Localisation Context. The Prague Bulletin of Mathematical Linguistics, 93(1).

Maja Popović. 2015. ChrF: Character n-gram F-score for automatic MT evaluation. In Ondřej Bojar, Rajan Chatterjee, Christian Federmann, Barry Haddow, Chris Hokamp, Matthias Huck, Varvara Logacheva, and Pavel Pecina, editors, Proceedings of the tenth workshop on statistical machine translation, pages 392–395, Lisbon, Portugal. Association for Computational Linguistics.

Maja Popović. 2020. Informative manual evaluation of machine translation output. In Donia Scott, Nuria Bel, and Chengqing Zong, editors, Proceedings of the 28th international conference on computational linguistics, pages 5059–5069, Barcelona, Spain (Online). International Committee on Computational Linguistics.

Matt Post. 2018. A call for clarity in reporting BLEU scores. In Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Lucia Specia, Marco Turchi, and Karin Verspoor, editors, Proceedings of the third conference on machine translation: Research papers, pages 186–191, Brussels, Belgium. Association for Computational Linguistics.

Marcelo OR Prates, Pedro H Avelar, and Luís C Lamb. 2020. Assessing gender bias in machine translation: A case study with Google Translate. Neural Computing and Applications, 32:6363–6381.

Jirui Qi^*, Gabriele Sarti^*, Raquel Fernández, and Arianna Bisazza. 2024. Model internals-based answer attribution for trustworthy retrieval-augmented generation. In Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen, editors, Proceedings of the 2024 conference on empirical methods in natural language processing, pages 6037–6053, Miami, Florida, USA. Association for Computational Linguistics.

Ella Rabinovich, Raj Nath Patel, Shachar Mirkin, Lucia Specia, and Shuly Wintner. 2017. Personalized machine translation: Preserving original author traits. In Mirella Lapata, Phil Blunsom, and Alexander Koller, editors, Proceedings of the 15th conference of the European chapter of the association for computational linguistics: Volume 1, long papers, pages 1074–1084, Valencia, Spain. Association for Computational Linguistics.

Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI Blog.

Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research, 21(140):1–67.

Daking Rai, Yilun Zhou, Shi Feng, Abulhair Saparov, and Ziyu Yao. 2024. A practical review of mechanistic interpretability for transformer-based language models. Arxiv Preprint.

Korbinian Randl, John Pavlopoulos, Aron Henriksson, and Tony Lindgren. 2025. Evaluating the reliability of self-explanations in large language models. In Discovery science: 27th international conference, pages 36–51, Berlin, Heidelberg. Springer-Verlag.

Sudha Rao and Joel Tetreault. 2018. Dear sir or madam, may I introduce the GYAFC dataset: Corpus, benchmarks and metrics for formality style transfer. In Marilyn Walker, Heng Ji, and Amanda Stent, editors, Proceedings of the 2018 conference of the north American chapter of the association for computational linguistics: Human language technologies, volume 1 (long papers), pages 129–140, New Orleans, Louisiana. Association for Computational Linguistics.

Hannah Rashkin, Vitaly Nikolaev, Matthew Lamm, Lora Aroyo, Michael Collins, Dipanjan Das, Slav Petrov, Gaurav Singh Tomar, Iulia Turc, and David Reitter. 2023. Measuring attribution in natural language generation models. Computational Linguistics, 49(4):777–840.

Tilman Räuker, Anson Ho, Stephen Casper, and Dylan Hadfield-Menell. 2023. Toward transparent AI: A survey on interpreting the inner structures of deep neural networks. In 2023 IEEE conference on secure and trustworthy machine learning (SaTML), pages 464–483.

Shauli Ravfogel, Yoav Goldberg, and Jacob Goldberger. 2023. Conformal nucleus sampling. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki, editors, Findings of the association for computational linguistics: ACL 2023, pages 27–34, Toronto, Canada. Association for Computational Linguistics.

Ricardo Rei, José G. C. de Souza, Duarte Alves, Chrysoula Zerva, Ana C Farinha, Taisiya Glushkova, Alon Lavie, Luisa Coheur, and André F. T. Martins. 2022a. COMET-22: Unbabel-IST 2022 submission for the metrics shared task. In Philipp Koehn, Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Tom Kocmi, André Martins, Makoto Morishita, et al., editors, Proceedings of the seventh conference on machine translation (WMT), pages 578–585, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.

Ricardo Rei, Ana C Farinha, José G. C. de Souza, Pedro G. Ramos, André F. T. Martins, Luisa Coheur, and Alon Lavie. 2022b. Searching for COMETINHO: The little metric that could. In Helena Moniz, Lieve Macken, Andrew Rufener, Loïc Barrault, Marta R. Costa-jussà, Christophe Declercq, Maarit Koponen, Ellie Kemp, Spyridon Pilos, Mikel L. Forcada, Carolina Scarton, Joachim Van den Bogaert, Joke Daems, Arda Tezcan, Bram Vanroy, and Margot Fonteyne, editors, Proceedings of the 23rd annual conference of the european association for machine translation, pages 61–70, Ghent, Belgium. European Association for Machine Translation.

Ricardo Rei, Ana C Farinha, Chrysoula Zerva, Daan van Stigt, Craig Stewart, Pedro Ramos, Taisiya Glushkova, André F. T. Martins, and Alon Lavie. 2021. Are references really needed? Unbabel-IST 2021 submission for the metrics shared task. In Loic Barrault, Ondrej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussa, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Tom Kocmi, Andre Martins, Makoto Morishita, et al., editors, Proceedings of the sixth conference on machine translation, pages 1030–1040, Online. Association for Computational Linguistics.

Ricardo Rei, Nuno M. Guerreiro, Marcos Treviso, Luisa Coheur, Alon Lavie, and André Martins. 2023. The inside story: Towards better understanding of machine translation neural evaluation metrics. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki, editors, Proceedings of the 61st annual meeting of the association for computational linguistics (volume 2: Short papers), pages 1089–1105, Toronto, Canada. Association for Computational Linguistics.

Ricardo Rei, Jose Pombal, Nuno M. Guerreiro, João Alves, Pedro Henrique Martins, Patrick Fernandes, Helena Wu, Tania Vaz, Duarte Alves, Amin Farajian, Sweta Agrawal, Antonio Farinhas, José G. C. De Souza, and André Martins. 2024. Tower v2: Unbabel-IST 2024 submission for the general MT shared task. In Barry Haddow, Tom Kocmi, Philipp Koehn, and Christof Monz, editors, Proceedings of the ninth conference on machine translation, pages 185–204, Miami, Florida, USA. Association for Computational Linguistics.

Ricardo Rei, Craig Stewart, Ana C Farinha, and Alon Lavie. 2020. COMET: A neural framework for MT evaluation. In Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu, editors, Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), pages 2685–2702, Online. Association for Computational Linguistics.

Emily Reif, Daphne Ippolito, Ann Yuan, Andy Coenen, Chris Callison-Burch, and Jason Wei. 2022. A recipe for arbitrary text style transfer with large language models. In Smaranda Muresan, Preslav Nakov, and Aline Villavicencio, editors, Proceedings of the 60th annual meeting of the association for computational linguistics (volume 2: Short papers), pages 837–848, Dublin, Ireland. Association for Computational Linguistics.

Ruiyang Ren, Yuhao Wang, Yingqi Qu, Wayne Xin Zhao, Jing Liu, Hua Wu, Ji-Rong Wen, and Haifeng Wang. 2025. Investigating the factual knowledge boundary of large language models with retrieval augmentation. In Owen Rambow, Leo Wanner, Marianna Apidianaki, Hend Al-Khalifa, Barbara Di Eugenio, and Steven Schockaert, editors, Proceedings of the 31st international conference on computational linguistics, pages 3697–3715, Abu Dhabi, UAE. Association for Computational Linguistics.

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. "Why should i trust you?": Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144, New York, NY, USA. Association for Computing Machinery.

Nina Rimsky, Nick Gabrieli, Julian Schulz, Meg Tong, Evan Hubinger, and Alexander Turner. 2024. Steering llama 2 via contrastive activation addition. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar, editors, Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: Long papers), pages 15504–15522, Bangkok, Thailand. Association for Computational Linguistics.

Anna Rogers, Olga Kovaleva, and Anna Rumshisky. 2020. A primer in BERTology: What we know about how BERT works. Transactions of the Association for Computational Linguistics, 8:842–866.

Raphael Rubino, Atsushi Fujita, and Benjamin Marie. 2021. Error identification for machine translation with metric embedding and attention. In Yang Gao, Steffen Eger, Wei Zhao, Piyawat Lertvittayakumjorn, and Marina Fomicheva, editors, Proceedings of the 2nd workshop on evaluation and comparison of NLP systems, pages 146–156, Punta Cana, Dominican Republic. Association for Computational Linguistics.

Cynthia Rudin. 2019. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1:206–215.

Rachel Rudinger, Jason Naradowsky, Brian Leonard, and Benjamin Van Durme. 2018. Gender bias in coreference resolution. In Marilyn Walker, Heng Ji, and Amanda Stent, editors, Proceedings of the 2018 conference of the north American chapter of the association for computational linguistics: Human language technologies, volume 2 (short papers), pages 8–14, New Orleans, Louisiana. Association for Computational Linguistics.

David E. Rumelhart and James L. McClelland. 1987. Learning internal representations by error propagation. In Parallel distributed processing: Explorations in the microstructure of cognition: foundations, pages 318–362. MIT Press.

Victor Sanh, Albert Webson, Colin Raffel, Stephen Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, et al. 2022. Multitask prompted training enables zero-shot task generalization. In Proceedings of the tenth international conference on learning representations (ICLR).

Soumya Sanyal and Xiang Ren. 2021. Discretized integrated gradients for explaining language models. In Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih, editors, Proceedings of the 2021 conference on empirical methods in natural language processing, pages 10285–10299, Online; Punta Cana, Dominican Republic. Association for Computational Linguistics.

Naomi Saphra and Sarah Wiegreffe. 2024. Mechanistic? In Yonatan Belinkov, Najoung Kim, Jaap Jumelet, Hosein Mohebbi, Aaron Mueller, and Hanjie Chen, editors, Proceedings of the 7th BlackboxNLP workshop: Analyzing and interpreting neural networks for NLP, pages 480–498, Miami, Florida, US. Association for Computational Linguistics.

Gabriele Sarti, Arianna Bisazza, Ana Guerberof-Arenas, and Antonio Toral. 2022. DivEMT: Neural machine translation post-editing effort across typologically diverse languages. In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang, editors, Proceedings of the 2022 conference on empirical methods in natural language processing, pages 7795–7816, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.

Gabriele Sarti, Tommaso Caselli, Arianna Bisazza, and Malvina Nissim. 2024a. EurekaRebus - verbalized rebus solving with LLMs: A CALAMITA challenge. In Felice Dell’Orletta, Alessandro Lenci, Simonetta Montemagni, and Rachele Sprugnoli, editors, Proceedings of the 10th italian conference on computational linguistics (CLiC-it 2024), pages 1202–1208, Pisa, Italy. CEUR Workshop Proceedings.

Gabriele Sarti, Tommaso Caselli, Malvina Nissim, and Arianna Bisazza. 2024b. Non verbis, sed rebus: Large language models are weak solvers of Italian rebuses. In Felice Dell’Orletta, Alessandro Lenci, Simonetta Montemagni, and Rachele Sprugnoli, editors, Proceedings of the 10th italian conference on computational linguistics (CLiC-it 2024), pages 888–897, Pisa, Italy. CEUR Workshop Proceedings.

Gabriele Sarti, Grzegorz Chrupała, Malvina Nissim, and Arianna Bisazza. 2024c. Quantifying the plausibility of context reliance in neural machine translation. In The twelfth international conference on learning representations (ICLR 2024), Vienna, Austria. OpenReview.

Gabriele Sarti, Nils Feldhus, Jirui Qi, Malvina Nissim, and Arianna Bisazza. 2024d. Democratizing advanced attribution analyses of generative language models with the inseq toolkit. In xAI-2024 late-breaking work, demos and doctoral consortium joint proceedings, pages 289–296, Valletta, Malta. CEUR.org.

Gabriele Sarti, Nils Feldhus, Ludwig Sickert, Oskar van der Wal, Malvina Nissim, and Arianna Bisazza. 2023a. Inseq: An interpretability toolkit for sequence generation models. In Danushka Bollegala, Ruihong Huang, and Alan Ritter, editors, Proceedings of the 61st annual meeting of the association for computational linguistics (volume 3: System demonstrations), pages 421–435, Toronto, Canada. Association for Computational Linguistics.

Gabriele Sarti, Phu Mon Htut, Xing Niu, Benjamin Hsu, Anna Currey, Georgiana Dinu, and Maria Nadejde. 2023b. RAMP: Retrieval and attribute-marking enhanced prompting for attribute-controlled translation. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki, editors, Proceedings of the 61st annual meeting of the association for computational linguistics (volume 2: Short papers), pages 1476–1490, Toronto, Canada. Association for Computational Linguistics.

Gabriele Sarti and Malvina Nissim. 2024. IT5: Text-to-text pretraining for Italian language understanding and generation. In Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, and Nianwen Xue, editors, Proceedings of the 2024 joint international conference on computational linguistics, language resources and evaluation (LREC-COLING 2024), pages 9422–9433, Torino, Italia. ELRA; ICCL.

Gabriele Sarti, Vilém Zouhar, Grzegorz Chrupała, Ana Guerberof-Arenas, Malvina Nissim, and Arianna Bisazza. 2025a. QE4PE: Word-level quality estimation for human post-editing. Transactions of the Association for Computational Linguistics, 13:1410–1435.

Gabriele Sarti, Vilém Zouhar, Malvina Nissim, and Arianna Bisazza. 2025b. Unsupervised word-level quality estimation for machine translation through the lens of annotators (dis)agreement. In Christos Christodoulopoulos, Tanmoy Chakraborty, Carolyn Rose, and Violet Peng, editors, Proceedings of the 2025 conference on empirical methods in natural language processing, pages 18320–18337, Suzhou, China. Association for Computational Linguistics.

Danielle Saunders and Bill Byrne. 2020. Reducing gender bias in neural machine translation as a domain adaptation problem. In Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel Tetreault, editors, Proceedings of the 58th annual meeting of the association for computational linguistics, pages 7724–7736, Online. Association for Computational Linguistics.

Beatrice Savoldi, Marco Gaido, Luisa Bentivogli, Matteo Negri, and Marco Turchi. 2021. Gender bias in machine translation. Transactions of the Association for Computational Linguistics, 9:845–874.

Beatrice Savoldi, Alan Ramponi, Matteo Negri, and Luisa Bentivogli. 2025. Translation in the hands of many: Centering lay users in machine translation interactions.

Daniel Scalena, Gabriele Sarti, and Malvina Nissim. 2024. Multi-property steering of large language models with dynamic activation composition. In Yonatan Belinkov, Najoung Kim, Jaap Jumelet, Hosein Mohebbi, Aaron Mueller, and Hanjie Chen, editors, Proceedings of the 7th BlackboxNLP workshop: Analyzing and interpreting neural networks for NLP, pages 577–603, Miami, Florida, US. Association for Computational Linguistics.

Daniel Scalena^*, Gabriele Sarti^*, Arianna Bisazza, Elisabetta Fersini, and Malvina Nissim. 2025. Steering large language models for machine translation personalization. Arxiv Preprint.

Andrea Schioppa, David Vilar, Artem Sokolov, and Katja Filippova. 2021. Controlling machine translation for multiple attributes with additive interventions. In Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih, editors, Proceedings of the 2021 conference on empirical methods in natural language processing, pages 6676–6696, Online; Punta Cana, Dominican Republic. Association for Computational Linguistics.

John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal policy optimization algorithms.

Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave, Armand Joulin, and Angela Fan. 2021. CCMatrix: Mining billions of high-quality parallel sentences on the web. In Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli, editors, Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (volume 1: Long papers), pages 6490–6500, Online. Association for Computational Linguistics.

Thibault Sellam, Dipanjan Das, and Ankur Parikh. 2020. BLEURT: Learning robust metrics for text generation. In Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel Tetreault, editors, Proceedings of the 58th annual meeting of the association for computational linguistics, pages 7881–7892, Online. Association for Computational Linguistics.

Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016a. Controlling politeness in neural machine translation via side constraints. In Kevin Knight, Ani Nenkova, and Owen Rambow, editors, Proceedings of the 2016 conference of the north American chapter of the association for computational linguistics: Human language technologies, pages 35–40, San Diego, California. Association for Computational Linguistics.

Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016b. Neural machine translation of rare words with subword units. In Katrin Erk and Noah A. Smith, editors, Proceedings of the 54th annual meeting of the association for computational linguistics (volume 1: Long papers), pages 1715–1725, Berlin, Germany. Association for Computational Linguistics.

Lee Sharkey, Bilal Chughtai, Joshua Batson, Jack Lindsey, Jeff Wu, Lucius Bushnaq, Nicholas Goldowsky-Dill, Stefan Heimersheim, Alejandro Ortega, Joseph Bloom, Stella Biderman, Adria Garriga-Alonso, Arthur Conmy, Neel Nanda, Jessica Rumbelow, Martin Wattenberg, Nandi Schoots, Joseph Miller, Eric J. Michaud, et al. 2025. Open problems in mechanistic interpretability.

Raksha Shenoy, Nico Herbig, Antonio Krüger, and Josef van Genabith. 2021. Investigating the helpfulness of word-level quality estimation for post-editing machine translation output. In Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih, editors, Proceedings of the 2021 conference on empirical methods in natural language processing, pages 10173–10185, Online; Punta Cana, Dominican Republic. Association for Computational Linguistics.

Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed Chi, Nathanael Schärli, and Denny Zhou. 2023. Large language models can be easily distracted by irrelevant context. In Proceedings of the 40th international conference on machine learning, Honolulu, Hawaii, USA. JMLR.org.

Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. 2017. Learning important features through propagating activation differences. In Doina Precup and Yee Whye Teh, editors, Proceedings of the 34th international conference on machine learning, volume 70, pages 3145–3153. PMLR.

Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2014. Deep inside convolutional networks: Visualising image classification models and saliency maps. In Yoshua Bengio and Yann LeCun, editors, 2nd international conference on learning representations, (ICLR), Banff, AB, Canada.

Koustuv Sinha, Prasanna Parthasarathi, Joelle Pineau, and Adina Williams. 2021. UnNatural Language Inference. In Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli, editors, Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (volume 1: Long papers), pages 7329–7346, Online. Association for Computational Linguistics.

Leon Sixt, Maximilian Granz, and Tim Landgraf. 2020. When explanations lie: Why many modified BP attributions fail. In Hal Daumé III and Aarti Singh, editors, Proceedings of the 37th international conference on machine learning, volume 119, pages 9046–9057. PMLR.

Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg. 2017. SmoothGrad: Removing noise by adding noise.

Paul Smolensky. 1986. Neural and conceptual interpretation of PDP models.

Matthew Snover, Bonnie Dorr, Rich Schwartz, Linnea Micciulla, and John Makhoul. 2006. A study of translation edit rate with targeted human annotation. In Proceedings of the 7th conference of the association for machine translation in the americas: Technical papers, pages 223–231, Cambridge, Massachusetts, USA. Association for Machine Translation in the Americas.

Rion Snow, Brendan O’Connor, Daniel Jurafsky, and Andrew Ng. 2008. Cheap and fast – but is it good? Evaluating non-expert annotations for natural language tasks. In Mirella Lapata and Hwee Tou Ng, editors, Proceedings of the 2008 conference on empirical methods in natural language processing, pages 254–263, Honolulu, Hawaii. Association for Computational Linguistics.

Lucia Specia, Frédéric Blain, Marina Fomicheva, Erick Fonseca, Vishrav Chaudhary, Francisco Guzmán, and André F. T. Martins. 2020. Findings of the WMT 2020 shared task on quality estimation. In Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Yvette Graham, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, et al., editors, Proceedings of the fifth conference on machine translation, pages 743–764, Online. Association for Computational Linguistics.

Lucia Specia, Carolina Scarton, Gustavo Henrique Paetzold, and Graeme Hirst. 2018. Quality estimation for machine translation. Morgan & Claypool Publishers.

Lucia Specia, Marco Turchi, Nicola Cancedda, Nello Cristianini, and Marc Dymetman. 2009. Estimating the sentence-level quality of machine translation systems. In Lluís Màrquez and Harold Somers, editors, Proceedings of the 13th annual conference of the european association for machine translation, Barcelona, Spain. European Association for Machine Translation.

Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(56):1929–1958.

Gabriel Stanovsky, Noah A. Smith, and Luke Zettlemoyer. 2019. Evaluating gender bias in machine translation. In Anna Korhonen, David Traum, and Lluís Màrquez, editors, Proceedings of the 57th annual meeting of the association for computational linguistics, pages 1679–1684, Florence, Italy. Association for Computational Linguistics.

Maria Stasimioti and Vilelmini Sosoni. 2020. Translation vs post-editing of NMT output: Insights from the English-Greek language pair. In John E. Ortega, Marcello Federico, Constantin Orasan, and Maja Popovic, editors, Proceedings of 1st workshop on post-editing in modern-day translation, pages 109–124, Virtual. Association for Machine Translation in the Americas.

Alessandro Stolfo, Ben Wu, Wes Gurnee, Yonatan Belinkov, Xingyi Song, Mrinmaya Sachan, and Neel Nanda. 2024. Confidence regulation neurons in language models. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang, editors, Advances in neural information processing systems, volume 37, pages 125019–125049. Curran Associates, Inc.

Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. 2024. RoFormer: Enhanced transformer with rotary position embedding. Neurocomputing, 568:127063.

Jiao Sun, Swabha Swayamdipta, Jonathan May, and Xuezhe Ma. 2022. Investigating the benefits of free-form rationales. In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang, editors, Findings of the association for computational linguistics: EMNLP 2022, pages 5867–5882, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.

Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017. Axiomatic attribution for deep networks. In Proceedings of the 34th international conference on machine learning (ICML), volume 70, pages 3319–3328, Sydney, Australia. Journal of Machine Learning Research (JMLR).

Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. In Proceedings of the 28th international conference on neural information processing systems - volume 2, pages 3104–3112, Cambridge, MA, USA. MIT Press.

Mirac Suzgun, Luke Melas-Kyriazi, and Dan Jurafsky. 2022. Prompt-and-rerank: A method for zero-shot and few-shot arbitrary textual style transfer with small language models. In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang, editors, Proceedings of the 2022 conference on empirical methods in natural language processing, pages 2195–2222, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.

Aleš Tamchyna. 2021. Deploying MT quality estimation on a large scale: Lessons learned and open questions. In Janice Campbell, Ben Huyck, Stephen Larocca, Jay Marciano, Konstantin Savenkov, and Alex Yanishevsky, editors, Proceedings of machine translation summit XVIII: Users and providers track, pages 291–305, Virtual. Association for Machine Translation in the Americas.

Joel Tang, Marina Fomicheva, and Lucia Specia. 2022. Reducing hallucinations in neural machine translation with feature attribution. ArXiv.

Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, and Angela Fan. 2021. Multilingual translation from denoising pre-training. In Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli, editors, Findings of the association for computational linguistics: ACL-IJCNLP 2021, pages 3450–3466, Online. Association for Computational Linguistics.

Gemma Team. 2024a. Gemma 2: Improving open language models at a practical size. Arxiv.

Llama Team. 2024b. The llama 3 herd of models. Arxiv.

Ian Tenney, Dipanjan Das, and Ellie Pavlick. 2019. BERT rediscovers the classical NLP pipeline. In Anna Korhonen, David Traum, and Lluís Màrquez, editors, Proceedings of the 57th annual meeting of the association for computational linguistics, pages 4593–4601, Florence, Italy. Association for Computational Linguistics.

Ian Tenney, Ryan Mullins, Bin Du, Shree Pandya, Minsuk Kahng, and Lucas Dixon. 2024. Interactive prompt debugging with sequence salience. Arxiv.

Ian Tenney, James Wexler, Jasmijn Bastings, Tolga Bolukbasi, Andy Coenen, Sebastian Gehrmann, Ellen Jiang, Mahima Pushkarna, Carey Radebaugh, Emily Reif, and Ann Yuan. 2020. The language interpretability tool: Extensible, interactive visualizations and analysis for NLP models. In Qun Liu and David Schlangen, editors, Proceedings of the 2020 conference on empirical methods in natural language processing: System demonstrations, pages 107–118, Online. Association for Computational Linguistics.

Katherine Thai, Marzena Karpinska, Kalpesh Krishna, Bill Ray, Moira Inghilleri, John Wieting, and Mohit Iyyer. 2022. Exploring document-level literary machine translation with parallel paragraphs from world literature. In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang, editors, Proceedings of the 2022 conference on empirical methods in natural language processing, pages 9882–9902, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.

Brian Thompson and Matt Post. 2020. Automatic machine translation evaluation in many languages via zero-shot paraphrasing. In Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu, editors, Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), pages 90–121, Online. Association for Computational Linguistics.

Jörg Tiedemann. 2020. The tatoeba translation challenge – realistic data sets for low resource and multilingual MT. In Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Yvette Graham, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, et al., editors, Proceedings of the fifth conference on machine translation, pages 1174–1182, Online. Association for Computational Linguistics.

Jörg Tiedemann and Yves Scherrer. 2017. Neural machine translation with extended context. In Bonnie Webber, Andrei Popescu-Belis, and Jörg Tiedemann, editors, Proceedings of the third workshop on discourse in machine translation, pages 82–92, Copenhagen, Denmark. Association for Computational Linguistics.

Jörg Tiedemann and Santhosh Thottingal. 2020. OPUS-MT – building open translation services for the world. In André Martins, Helena Moniz, Sara Fumega, Bruno Martins, Fernando Batista, Luisa Coheur, Carla Parra, Isabel Trancoso, Marco Turchi, Arianna Bisazza, Joss Moorkens, Ana Guerberof, Mary Nurminen, Lena Marg, and Mikel L. Forcada, editors, Proceedings of the 22nd annual conference of the european association for machine translation, pages 479–480, Lisboa, Portugal. European Association for Machine Translation.

Curt Tigges, Oskar J. Hollinsworth, Atticus Geiger, and Neel Nanda. 2024. Language models linearly represent sentiment. In Yonatan Belinkov, Najoung Kim, Jaap Jumelet, Hosein Mohebbi, Aaron Mueller, and Hanjie Chen, editors, Proceedings of the 7th BlackboxNLP workshop: Analyzing and interpreting neural networks for NLP, pages 58–87, Miami, Florida, US. Association for Computational Linguistics.

Eric Todd, Millicent L. Li, Arnab Sen Sharma, Aaron Mueller, Byron C. Wallace, and David Bau. 2024. Function vectors in large language models. In Proceedings of the 2024 international conference on learning representations.

Antonio Toral, Sheila Castilho, Ke Hu, and Andy Way. 2018a. Attaining the unattainable? Reassessing claims of human parity in neural machine translation. In Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Lucia Specia, Marco Turchi, and Karin Verspoor, editors, Proceedings of the third conference on machine translation: Research papers, pages 113–123, Brussels, Belgium. Association for Computational Linguistics.

Antonio Toral and Andy Way. 2015. Translating literary text between related languages using SMT. In Anna Feldman, Anna Kazantseva, Stan Szpakowicz, and Corina Koolen, editors, Proceedings of the fourth workshop on computational linguistics for literature, pages 123–132, Denver, Colorado, USA. Association for Computational Linguistics.

Antonio Toral and Andy Way. 2018. What level of quality can neural machine translation attain on literary text? In Translation quality assessment: From principles to practice, pages 263–287. Springer International Publishing, Cham.

Antonio Toral, Martijn Wieling, and Andy Way. 2018b. Post-editing effort of a novel with statistical and neural machine translation. Frontiers in Digital Humanities, 5:1–11.

Hugo Touvron, Louis Martin, Kevin R. Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Daniel M. Bikel, Lukas Blecher, Cristian Cantòn Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, et al. 2023. Llama 2: Open foundation and fine-tuned chat models. ArXiv.

Lewis Tunstall, Edward Beeching, Nathan Lambert, Nazneen Rajani, Kashif Rasul, Younes Belkada, Shengyi Huang, Leandro von Werra, Clémentine Fourrier, Nathan Habib, Nathan Sarrazin, Omar Sanseviero, Alexander M. Rush, and Thomas Wolf. 2024. Zephyr: Direct distillation of LM alignment. In Proceedings of the 1st conference on language modeling (COLM).

Marco Turchi, Antonios Anastasopoulos, José G. C. de Souza, and Matteo Negri. 2014. Adaptive quality estimation for machine translation. In Kristina Toutanova and Hua Wu, editors, Proceedings of the 52nd annual meeting of the association for computational linguistics (volume 1: Long papers), pages 710–720, Baltimore, Maryland. Association for Computational Linguistics.

Marco Turchi, Matteo Negri, M. Amin Farajian, and Marcello Federico. 2017. Continuous learning from human post-edits for neural machine translation. The Prague Bulletin of Mathematical Linguistics, 108:233–244.

Marco Turchi, Matteo Negri, and Marcello Federico. 2013. Coping with the subjectivity of human judgements in MT quality estimation. In Ondrej Bojar, Christian Buck, Chris Callison-Burch, Barry Haddow, Philipp Koehn, Christof Monz, Matt Post, Herve Saint-Amand, Radu Soricut, and Lucia Specia, editors, Proceedings of the eighth workshop on statistical machine translation, pages 240–251, Sofia, Bulgaria. Association for Computational Linguistics.

Miles Turpin, Julian Michael, Ethan Perez, and Samuel R. Bowman. 2023. Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting. In Proceedings of the 37th international conference on neural information processing systems, Red Hook, NY, USA. Curran Associates Inc.

Dennis Ulmer, Jes Frellsen, and Christian Hardmeier. 2022. Exploring predictive uncertainty and calibration in NLP: A study on the impact of method & data scarcity. In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang, editors, Findings of the association for computational linguistics: EMNLP 2022, pages 2707–2735, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.

Alexandra N Uma, Tommaso Fornaciari, Dirk Hovy, Silviu Paun, Barbara Plank, and Massimo Poesio. 2021. Learning from disagreement: A survey. Journal of Artificial Intelligence Research, 72:1385–1470.

Ahmet Üstün, Viraat Aryabumi, Zheng Yong, Wei-Yin Ko, Daniel D’souza, Gbemileke Onilude, Neel Bhandari, Shivalika Singh, Hui-Lee Ooi, Amr Kayid, Freddie Vargus, Phil Blunsom, Shayne Longpre, Niklas Muennighoff, Marzieh Fadaee, Julia Kreutzer, and Sara Hooker. 2024. Aya model: An instruction finetuned open-access multilingual language model. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar, editors, Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: Long papers), pages 15894–15939, Bangkok, Thailand. Association for Computational Linguistics.

Keyon Vafa, Yuntian Deng, David Blei, and Alexander Rush. 2021. Rationales for sequential predictions. In Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih, editors, Proceedings of the 2021 conference on empirical methods in natural language processing, pages 10314–10332, Online; Punta Cana, Dominican Republic. Association for Computational Linguistics.

Jannis Vamvas and Rico Sennrich. 2021a. Contrastive conditioning for assessing disambiguation in MT: A case study of distilled bias. In Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih, editors, Proceedings of the 2021 conference on empirical methods in natural language processing, pages 10246–10265, Online; Punta Cana, Dominican Republic. Association for Computational Linguistics.

Jannis Vamvas and Rico Sennrich. 2021b. On the limits of minimal pairs in contrastive evaluation. In Jasmijn Bastings, Yonatan Belinkov, Emmanuel Dupoux, Mario Giulianelli, Dieuwke Hupkes, Yuval Pinter, and Hassan Sajjad, editors, Proceedings of the fourth BlackboxNLP workshop on analyzing and interpreting neural networks for NLP, pages 58–68, Punta Cana, Dominican Republic. Association for Computational Linguistics.

Jannis Vamvas and Rico Sennrich. 2022. As little as possible, as much as necessary: Detecting over- and undertranslations with contrastive conditioning. In Smaranda Muresan, Preslav Nakov, and Aline Villavicencio, editors, Proceedings of the 60th annual meeting of the association for computational linguistics (volume 2: Short papers), pages 490–500, Dublin, Ireland. Association for Computational Linguistics.

Eva Vanmassenhove, Christian Hardmeier, and Andy Way. 2018. Getting gender right in neural machine translation. In Ellen Riloff, David Chiang, Julia Hockenmaier, and Jun’ichi Tsujii, editors, Proceedings of the 2018 conference on empirical methods in natural language processing, pages 3003–3008, Brussels, Belgium. Association for Computational Linguistics.

Vladimir N. Vapnik. 1995. The nature of statistical learning theory. Springer-Verlag New York, Inc.

Helena Vasconcelos, Gagan Bansal, Adam Fourney, Q. Vera Liao, and Jennifer Wortman Vaughan. 2025. Generation probabilities are not enough: Uncertainty highlighting in AI code completions. ACM Trans. Comput.-Hum. Interact., 32(1).

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in neural information processing systems, volume 30. Curran Associates, Inc.

David Vilar, Markus Freitag, Colin Cherry, Jiaming Luo, Viresh Ratnakar, and George Foster. 2023. Prompting PaLM for translation: Assessing strategies and performance. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki, editors, Proceedings of the 61st annual meeting of the association for computational linguistics (volume 1: Long papers), pages 15406–15427, Toronto, Canada. Association for Computational Linguistics.

Rob Voigt and Dan Jurafsky. 2012. Towards a literary machine translation: The role of referential cohesion. In David Elson, Anna Kazantseva, Rada Mihalcea, and Stan Szpakowicz, editors, Proceedings of the NAACL-HLT 2012 workshop on computational linguistics for literature, pages 18–25, Montréal, Canada. Association for Computational Linguistics.

Elena Voita, Rico Sennrich, and Ivan Titov. 2019a. Context-aware monolingual repair for neural machine translation. In Kentaro Inui, Jing Jiang, Vincent Ng, and Xiaojun Wan, editors, Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pages 877–886, Hong Kong, China. Association for Computational Linguistics.

Elena Voita, Rico Sennrich, and Ivan Titov. 2019b. When a good translation is wrong in context: Context-aware machine translation improves on deixis, ellipsis, and lexical cohesion. In Anna Korhonen, David Traum, and Lluís Màrquez, editors, Proceedings of the 57th annual meeting of the association for computational linguistics, pages 1198–1212, Florence, Italy. Association for Computational Linguistics.

Elena Voita, Rico Sennrich, and Ivan Titov. 2021. Analyzing the source and target contributions to predictions in neural machine translation. In Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli, editors, Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (volume 1: Long papers), pages 1126–1140, Online. Association for Computational Linguistics.

Elena Voita, Pavel Serdyukov, Rico Sennrich, and Ivan Titov. 2018. Context-aware neural machine translation learns anaphora resolution. In Iryna Gurevych and Yusuke Miyao, editors, Proceedings of the 56th annual meeting of the association for computational linguistics (volume 1: Long papers), pages 1264–1274, Melbourne, Australia. Association for Computational Linguistics.

Elena Voita, David Talbot, Fedor Moiseev, Rico Sennrich, and Ivan Titov. 2019c. Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned. In Anna Korhonen, David Traum, and Lluís Màrquez, editors, Proceedings of the 57th annual meeting of the association for computational linguistics, pages 5797–5808, Florence, Italy. Association for Computational Linguistics.

Elizabeth Wagner. 1983. Rapid post-editing of systran. In Veronica Lawson, editor, Proceedings of translating and the computer 5: Tools for the trade, London, UK. Aslib.

Eric Wallace, Matt Gardner, and Sameer Singh. 2020. Interpreting predictions of NLP models. In Aline Villavicencio and Benjamin Van Durme, editors, Proceedings of the 2020 conference on empirical methods in natural language processing: Tutorial abstracts, pages 20–23, Online. Association for Computational Linguistics.

Longyue Wang, Siyou Liu, Chenyang Lyu, Wenxiang Jiao, Xing Wang, Jiahao Xu, Zhaopeng Tu, Yan Gu, Weiyu Chen, Minghao Wu, Liting Zhou, Philipp Koehn, Andy Way, and Yulin Yuan. 2024a. Findings of the WMT 2024 shared task on discourse-level literary translation. In Barry Haddow, Tom Kocmi, Philipp Koehn, and Christof Monz, editors, Proceedings of the ninth conference on machine translation, pages 699–700, Miami, Florida, USA. Association for Computational Linguistics.

Longyue Wang, Chenyang Lyu, Tianbo Ji, Zhirui Zhang, Dian Yu, Shuming Shi, and Zhaopeng Tu. 2023a. Document-level machine translation with large language models. In Houda Bouamor, Juan Pino, and Kalika Bali, editors, Proceedings of the 2023 conference on empirical methods in natural language processing, pages 16646–16661, Singapore. Association for Computational Linguistics.

Longyue Wang, Zhaopeng Tu, Yan Gu, Siyou Liu, Dian Yu, Qingsong Ma, Chenyang Lyu, Liting Zhou, Chao-Hong Liu, Yufeng Ma, Weiyu Chen, Yvette Graham, Bonnie Webber, Philipp Koehn, Andy Way, Yulin Yuan, and Shuming Shi. 2023b. Findings of the WMT 2023 shared task on discourse-level literary translation: A fresh orb in the cosmos of LLMs. In Philipp Koehn, Barry Haddow, Tom Kocmi, and Christof Monz, editors, Proceedings of the eighth conference on machine translation, pages 55–67, Singapore. Association for Computational Linguistics.

Weiyue Wang, Jan-Thorsten Peter, Hendrik Rosendahl, and Hermann Ney. 2016. CharacTer: Translation edit rate on character level. In Ondřej Bojar, Christian Buck, Rajen Chatterjee, Christian Federmann, Liane Guillou, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Aurélie Névéol, Mariana Neves, Pavel Pecina, Martin Popel, Philipp Koehn, Christof Monz, Matteo Negri, Matt Post, Lucia Specia, Karin Verspoor, Jörg Tiedemann, et al., editors, Proceedings of the first conference on machine translation: Volume 2, shared task papers, pages 505–510, Berlin, Germany. Association for Computational Linguistics.

Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou. 2020. MINILM: Deep self-attention distillation for task-agnostic compression of pre-trained transformers. In Proceedings of the 34th international conference on neural information processing systems, Red Hook, NY, USA. Curran Associates Inc.

Yifan Wang, Zewei Sun, Shanbo Cheng, Weiguo Zheng, and Mingxuan Wang. 2023c. Controlling styles in neural machine translation with activation prompt. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki, editors, Findings of the association for computational linguistics: ACL 2023, pages 2606–2620, Toronto, Canada. Association for Computational Linguistics.

Yue Wang, Cuong Hoang, and Marcello Federico. 2021. Towards modeling the style of translators in neural machine translation. In Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, and Yichao Zhou, editors, Proceedings of the 2021 conference of the north american chapter of the association for computational linguistics: Human language technologies, pages 1193–1199, Online. Association for Computational Linguistics.

Yuxia Wang, Jonibek Mansurov, Petar Ivanov, Jinyan Su, Artem Shelmanov, Akim Tsvigun, Osama Mohammed Afzal, Tarek Mahmoud, Giovanni Puccetti, and Thomas Arnold. 2024b. SemEval-2024 task 8: Multidomain, multimodel and multilingual machine-generated text detection. In Atul Kr. Ojha, A. Seza Doğruöz, Harish Tayyar Madabushi, Giovanni Da San Martino, Sara Rosenthal, and Aiala Rosá, editors, Proceedings of the 18th international workshop on semantic evaluation (SemEval-2024), pages 2057–2079, Mexico City, Mexico. Association for Computational Linguistics.

Yuxia Wang, Jonibek Mansurov, Petar Ivanov, Jinyan Su, Artem Shelmanov, Akim Tsvigun, Osama Mohammed Afzal, Tarek Mahmoud, Giovanni Puccetti, Thomas Arnold, Alham Aji, Nizar Habash, Iryna Gurevych, and Preslav Nakov. 2024c. M4GT-bench: Evaluation benchmark for black-box machine-generated text detection. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar, editors, Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: Long papers), pages 3964–3992, Bangkok, Thailand. Association for Computational Linguistics.

Alex Warstadt, Alicia Parrish, Haokun Liu, Anhad Mohananey, Wei Peng, Sheng-Fu Wang, and Samuel R. Bowman. 2020. BLiMP: The benchmark of linguistic minimal pairs for English. Transactions of the Association for Computational Linguistics, 8:377–392.

Leon Weber-Genzel, Siyao Peng, Marie-Catherine De Marneffe, and Barbara Plank. 2024. VariErr NLI: Separating annotation error from human label variation. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar, editors, Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: Long papers), pages 2256–2269, Bangkok, Thailand. Association for Computational Linguistics.

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc V. Le, and Denny Zhou. 2022. Chain-of-thought prompting elicits reasoning in large language models. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Advances in neural information processing systems, volume 35, pages 24824–24837. Curran Associates, Inc.

Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel. 2018. Constructing datasets for multi-hop reading comprehension across documents. Transactions of the Association for Computational Linguistics, 6:287–302.

John S. White, Theresa A. O’Connell, and Francis E. O’Mara. 1994. The ARPA MT evaluation methodologies: Evolution, lessons, and future approaches. In Proceedings of the first conference of the association for machine translation in the americas, Columbia, Maryland, USA.

Sarah Wiegreffe and Yuval Pinter. 2019. Attention is not not explanation. In Kentaro Inui, Jing Jiang, Vincent Ng, and Xiaojun Wan, editors, Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pages 11–20, Hong Kong, China. Association for Computational Linguistics.

Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, et al. 2020. Transformers: State-of-the-art natural language processing. In Qun Liu and David Schlangen, editors, Proceedings of the 2020 conference on empirical methods in natural language processing: System demonstrations, pages 38–45, Online. Association for Computational Linguistics.

Minghao Wu, Jiahao Xu, Yulin Yuan, Gholamreza Haffari, Longyue Wang, Weihua Luo, and Kaifu Zhang. 2025. (Perhaps) beyond human translation: Harnessing multi-agent collaboration for translating ultra-long literary texts. Arxiv.

Zhengxuan Wu, Aryaman Arora, Zheng Wang, Atticus Geiger, Dan Jurafsky, Christopher D. Manning, and Christopher Potts. 2024. ReFT: Representation finetuning for language models. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang, editors, Advances in neural information processing systems, volume 37, pages 63908–63962. Curran Associates, Inc.

Aris Xanthos, Sabine Laaha, Steven Gillis, Ursula Stephany, Ayhan Aksu-Koç, Anastasia Christofidou, Natalia Gagarina, Gordana Hrzica, F. Nihan Ketrez, Marianne Kilani-Schoch, Katharina Korecky-Kröll, Melita Kovačević, Klaus Laalo, Marijan Palmović, Barbara Pfeiler, Maria D. Voeikova, and Wolfgang U. Dressler. 2011. On the role of morphological richness in the early development of noun and verb inflection. First Language, 31(4):461–479.

Fangyuan Xu, Yixiao Song, Mohit Iyyer, and Eunsol Choi. 2023a. A critical evaluation of evaluations for long-form question answering. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki, editors, Proceedings of the 61st annual meeting of the association for computational linguistics (volume 1: Long papers), pages 3225–3245, Toronto, Canada. Association for Computational Linguistics.

Haoran Xu, Young Jin Kim, Amr Sharaf, and Hany Hassan Awadalla. 2024. A paradigm shift in machine translation: Boosting translation performance of large language models. In The twelfth international conference on learning representations.

Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. 2015. Show, attend and tell: Neural image caption generation with visual attention. In Francis Bach and David Blei, editors, Proceedings of the 32nd international conference on machine learning, volume 37, pages 2048–2057, Lille, France. PMLR.

Weijia Xu, Sweta Agrawal, Eleftheria Briakou, Marianna J. Martindale, and Marine Carpuat. 2023b. Understanding and detecting hallucinations in neural machine translation via model introspection. Transactions of the Association for Computational Linguistics, 11:546–564.

Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2021. MT5: A massively multilingual pre-trained text-to-text transformer. In Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, and Yichao Zhou, editors, Proceedings of the 2021 conference of the north american chapter of the association for computational linguistics: Human language technologies, pages 483–498, Online. Association for Computational Linguistics.

Zhen Yang, Fandong Meng, Yuanmeng Yan, and Jie Zhou. 2023. Rethinking the word-level quality estimation for machine translation from human judgement. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki, editors, Findings of the association for computational linguistics: ACL 2023, pages 2012–2025, Toronto, Canada. Association for Computational Linguistics.

Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018. HotpotQA: A dataset for diverse, explainable multi-hop question answering. In Ellen Riloff, David Chiang, Julia Hockenmaier, and Jun’ichi Tsujii, editors, Proceedings of the 2018 conference on empirical methods in natural language processing, pages 2369–2380, Brussels, Belgium. Association for Computational Linguistics.

Kayo Yin, Patrick Fernandes, Danish Pruthi, Aditi Chaudhary, André F. T. Martins, and Graham Neubig. 2021. Do context-aware translation models pay the right attention? In Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli, editors, Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (volume 1: Long papers), pages 788–801, Online. Association for Computational Linguistics.

Kayo Yin and Graham Neubig. 2022. Interpreting language models with contrastive explanations. In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang, editors, Proceedings of the 2022 conference on empirical methods in natural language processing, pages 184–198, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.

Alexander Yom Din, Taelin Karidi, Leshem Choshen, and Mor Geva. 2024. Jump to conclusions: Short-cutting transformers with linear transformations. In Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, and Nianwen Xue, editors, Proceedings of the 2024 joint international conference on computational linguistics, language resources and evaluation (LREC-COLING 2024), pages 9615–9625, Torino, Italia. ELRA; ICCL.

Wu Youyou, Michal Kosinski, and David Stillwell. 2015. Computer-based personality judgments are more accurate than those made by humans. Proceedings of the National Academy of Sciences, 112(4):1036–1040.

Xiang Yue, Boshi Wang, Ziru Chen, Kai Zhang, Yu Su, and Huan Sun. 2023. Automatic evaluation of attribution by large language models. In Houda Bouamor, Juan Pino, and Kalika Bali, editors, Findings of the association for computational linguistics: EMNLP 2023, pages 4615–4635, Singapore. Association for Computational Linguistics.

Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cedric Archambeau, Sanjiv Das, and Krishnaram Kenthapadi. 2021. On the lack of robust interpretability of neural text classifiers. In Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli, editors, Findings of the association for computational linguistics: ACL-IJCNLP 2021, pages 3730–3740, Online. Association for Computational Linguistics.

Matthew D. Zeiler and Rob Fergus. 2014. Visualizing and understanding convolutional networks. In David Fleet, Tomas Pajdla, Bernt Schiele, and Tinne Tuytelaars, editors, 13th european conference on computer vision (ECCV), pages 818–833, Switzerland. Springer International Publishing.

Matthew D. Zeiler, Graham W. Taylor, and Rob Fergus. 2011. Adaptive deconvolutional networks for mid and high level feature learning. In 2011 international conference on computer vision (ICCV), pages 2018–2025.

Chrysoula Zerva, Frederic Blain, José G. C. De Souza, Diptesh Kanojia, Sourabh Deoghare, Nuno M. Guerreiro, Giuseppe Attanasio, Ricardo Rei, Constantin Orasan, Matteo Negri, Marco Turchi, Rajen Chatterjee, Pushpak Bhattacharyya, Markus Freitag, and André Martins. 2024. Findings of the quality estimation shared task at WMT 2024: Are LLMs closing the gap in QE? In Barry Haddow, Tom Kocmi, Philipp Koehn, and Christof Monz, editors, Proceedings of the ninth conference on machine translation, pages 82–109, Miami, Florida, USA. Association for Computational Linguistics.

Chrysoula Zerva, Frédéric Blain, Ricardo Rei, Piyawat Lertvittayakumjorn, José G. C. de Souza, Steffen Eger, Diptesh Kanojia, Duarte Alves, Constantin Orăsan, Marina Fomicheva, André F. T. Martins, and Lucia Specia. 2022. Findings of the WMT 2022 shared task on quality estimation. In Philipp Koehn, Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Tom Kocmi, André Martins, Makoto Morishita, et al., editors, Proceedings of the seventh conference on machine translation (WMT), pages 69–99, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.

Chrysoula Zerva and André F. T. Martins. 2024. Conformalizing machine translation evaluation. Transactions of the Association for Computational Linguistics, 12:1460–1478.

Biao Zhang and Rico Sennrich. 2019. Root mean square layer normalization. In Proceedings of the 33rd international conference on neural information processing systems, Red Hook, NY, USA. Curran Associates Inc.

Jiacheng Zhang, Huanbo Luan, Maosong Sun, Feifei Zhai, Jingfang Xu, Min Zhang, and Yang Liu. 2018. Improving the transformer translation model with document-level context. In Ellen Riloff, David Chiang, Julia Hockenmaier, and Jun’ichi Tsujii, editors, Proceedings of the 2018 conference on empirical methods in natural language processing, pages 533–542, Brussels, Belgium. Association for Computational Linguistics.

Peng Zhang, Zhengqing Guan, Baoxi Liu, Xianghua (Sharon) Ding, Tun Lu, Hansu Gu, and Ning Gu. 2022. Building user-oriented personalized machine translator based on user-generated textual content. Proc. ACM Hum.-Comput. Interact., 6(CSCW2).

Mengjie Zhao and Hinrich Schütze. 2021. Discrete and soft prompting for multilingual models. In Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih, editors, Proceedings of the 2021 conference on empirical methods in natural language processing, pages 8547–8555, Online; Punta Cana, Dominican Republic. Association for Computational Linguistics.

Yao Zhao, Mikhail Khalman, Rishabh Joshi, Shashi Narayan, Mohammad Saleh, and Peter J Liu. 2023. Calibrating sequence likelihood improves conditional language generation. In The eleventh international conference on learning representations.

Yu Zhao, Alessio Devoto, Giwon Hong, Xiaotang Du, Aryo Pradipta Gema, Hongru Wang, Xuanli He, Kam-Fai Wong, and Pasquale Minervini. 2025. Steering knowledge selection behaviours in LLMs via SAE-based representation engineering. In Luis Chiruzzo, Alan Ritter, and Lu Wang, editors, Proceedings of the 2025 conference of the nations of the americas chapter of the association for computational linguistics: Human language technologies (volume 1: Long papers), pages 5117–5136, Albuquerque, New Mexico. Association for Computational Linguistics.

Zhixue Zhao and Boxuan Shan. 2024. ReAGent: A model-agnostic feature attribution method for generative language models. AAAI Workshop on Responsible Language Models (ReLM).

Meng Zhou, Xin Li, Yue Jiang, and Lidong Bing. 2023. Enhancing cross-lingual prompting with dual prompt augmentation. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki, editors, Findings of the association for computational linguistics: ACL 2023, pages 11008–11020, Toronto, Canada. Association for Computational Linguistics.

Zhemin Zhu, Delphine Bernhard, and Iryna Gurevych. 2010. A monolingual tree-based translation model for sentence simplification. In Chu-Ren Huang and Dan Jurafsky, editors, Proceedings of the 23rd international conference on computational linguistics (coling 2010), pages 1353–1361, Beijing, China. Coling 2010 Organizing Committee.

Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, Shashwat Goel, Nathaniel Li, Michael J. Byun, Zifan Wang, Alex Mallen, Steven Basart, Sanmi Koyejo, Dawn Song, Matt Fredrikson, et al. 2024. Enhancing neural network transparency through representation analysis. OpenReview.

Vilém Zouhar, Shuoyang Ding, Anna Currey, Tatyana Badeka, Jenyuan Wang, and Brian Thompson. 2024. Fine-tuned machine translation metrics struggle in unseen domains. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar, editors, Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 2: Short papers), pages 488–500, Bangkok, Thailand. Association for Computational Linguistics.

Vilém Zouhar, Tom Kocmi, and Mrinmaya Sachan. 2025. AI-assisted human evaluation of machine translation. In Luis Chiruzzo, Alan Ritter, and Lu Wang, editors, Proceedings of the 2025 conference of the nations of the americas chapter of the association for computational linguistics: Human language technologies (volume 1: Long papers), pages 4936–4950, Albuquerque, New Mexico. Association for Computational Linguistics.

Vilém Zouhar, Michal Novák, Matúš Žilinec, Ondřej Bojar, Mateo Obregón, Robin L. Hill, Frédéric Blain, Marina Fomicheva, Lucia Specia, and Lisa Yankovskaya. 2021a. Backtranslation feedback improves user confidence in MT, not quality. In Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, and Yichao Zhou, editors, Proceedings of the 2021 conference of the north american chapter of the association for computational linguistics: Human language technologies, pages 151–161, Online. Association for Computational Linguistics.

Vilém Zouhar, Martin Popel, Ondřej Bojar, and Aleš Tamchyna. 2021b. Neural machine translation quality and post-editing performance. In Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih, editors, Proceedings of the 2021 conference on empirical methods in natural language processing, pages 10204–10214, Online; Punta Cana, Dominican Republic. Association for Computational Linguistics.