DecoderLens: Layerwise Interpretation of Encoder-Decoder Transformers
We propose DecoderLens, a method to evaluate the iterative refinement of representations in encoder-decoder Transformer models.
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.
Are Character-level Translations Worth the Wait? Comparing Character- and Subword-level Models for Machine Translation
We analyze input contributions of char-level MT models and show how they modulate word and character-level information.
Inseq: An Interpretability Toolkit for Sequence Generation Models
We present Inseq, a Python library to democratize access to interpretability analyses of sequence generation models.
That Looks Hard: Characterizing Linguistic Complexity in Humans and Language Models
This paper investigates the relationship between two complementary perspectives in the human assessment of sentence complexity and how …
UmBERTo-MTSA@ AcCompl-It: Improving Complexity and Acceptability Prediction with Multi-task Learning on Self-Supervised Annotations
This work describes a self-supervised data augmentation approach used to improve learning models’ performances when only a …
Interpreting Neural Language Models for Linguistic Complexity Assessment
This thesis presents a model-driven study of multiple phenomena associated with linguistic complexity, and how those get encoded by …
Probing Linguistic Knowledge in Italian Neural Language Models across Language Varieties
We investigate whether and how using different architectures of probing models affects the performance of Italian transformers in …
