Deep Learning | Gabriele Sarti

Deep Learning

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.

Inseq: An Interpretability Toolkit for Sequence Generation Models

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

IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation

IT5s are the first encoder-decoder transformers pretrained on more than 40 billion Italian words.

Characterizing Linguistic Complexity in Humans and Language Models

Presenting my work on studying different metrics of linguistic complexity and how they correlate with linguistic phenomena and learned representations in neural language models

Contrastive Language-Image Pre-training for the Italian Language

We present the first CLIP model for the Italian Language (CLIP-Italian), trained on more than 1.4 million image-text pairs.

Contrastive Image-Text Pretraining for Italian

The first CLIP model pretrained on the Italian language.