Language Modeling

This talk introduces the Inseq toolkit for interpreting sequence generation models. The usage of Inseq is illustrated with examples introducing state-of-the-art approaches for interpreting language models such as contrastive attribution, tuned lenses and causal mediation analysis.

This talk introduces the Inseq toolkit for interpreting sequence generation models. The usage of Inseq is illustrated with examples introducing state-of-the-art approaches for interpreting language models such as contrastive attribution, tuned lenses and causal mediation analysis.

This thesis presents a model-driven study of multiple phenomena associated with linguistic complexity, and how those get encoded by neural language models' learned representations.

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

An overview of the latest advances in the field of NLP, with a focus on neural models and language understanding.

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