Gabriele Sarti
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Gabriele Sarti

PhD in Natural Language Processing

CLCG, University of Groningen

About me

Welcome to my website! 👋 I am a PhD student at the Computational Linguistics Group of the University of Groningen and member of the InDeep consortium, working on user-centric interpretability for neural machine translation. I am also the main developer of the Inseq library. My supervisors are Arianna Bisazza, Malvina Nissim and Grzegorz Chrupała.

Previously, I was a research intern at Amazon Translate NYC, a research scientist at Aindo, a Data Science MSc student at the University of Trieste and a co-founder of the AI Student Society.

My research focuses on interpretability for generative language models, with a particular interest to end-users’ benefits and the usage of human behavioral signals. I am also into causality topics and open source collaboration.

Your (anonymous) feedback is always welcome! 🙂

Interests

  • Conditional Text Generation
  • Interpretability for Deep Learning
  • Behavioral Data for NLP
  • Causality and Uncertainty Estimation

Education

Experience

🗞️ News

 

  • Inseq, our open-source toolkit for post-hoc interpretability of generative language models, is now available on Github! 🐛 We also have a demo paper with some usage examples.

Selected Publications

 

A Primer on the Inner Workings of Transformer-based Language Models

This primer provides a concise technical introduction to the current techniques used to interpret the inner workings of …

Quantifying the Plausibility of Context Reliance in Neural Machine Translation

We introduce PECoRe, an interpretability framework for identifying context dependence in language model generations.

RAMP: Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translation

We introduce Retrieval and Attribute-Marking enhanced Prompting (RAMP) to perform attribute-controlled MT with multilingual LLMs.

Blog posts

 

ICLR 2020 Trends: Better & Faster Transformers for Natural Language Processing

A summary of promising directions from ICLR 2020 for better and faster pretrained tranformers language models.

Recent & Upcoming Talks

Quantifying the Plausibility of Context Reliance in Neural Machine Translation
Explaining Language Models with Inseq

Projects

 

Inseq: An Interpretability Toolkit for Sequence Generation Models

An open-source library to democratize access to model interpretability for sequence generation models

PECoRe: Plausibility Evaluation of Context Usage in Language Models

An interpretability framework to detect and attribute context usage in language models’ generations

Contrastive Image-Text Pretraining for Italian

The first CLIP model pretrained on the Italian language.

Covid-19 Semantic Browser

A semantic browser for SARS-CoV-2 and COVID-19 powered by neural language models.

AItalo Svevo: Letters from an Artificial Intelligence

Generating letters with a neural language model in the style of Italo Svevo, a famous italian writer of the 20th century.

Histopathologic Cancer Detection with Neural Networks

A journey into the state of the art of histopathologic cancer detection approaches.