Interpretability | Gabriele Sarti

Interpretability

Advanced XAI Techniques and Inseq: An Interpretability Toolkit for Sequence Generation Models

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.

Introducing Inseq: An Interpretability Toolkit for Sequence Generation Models

After motivating the usage of interpretability methods in NLP, this talk introduces the Inseq toolkit for interpreting sequence generation models. The usage of Inseq is illustrated on two case studies related to gender bias in machine translation and locating factual knowledge withing GPT-2 representations.

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

PECoRe: Plausibility Evaluation of Context Usage in Language Models

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

Towards User-centric Interpretability of Machine Translation Models

With the astounding advances of artificial intelligence in recent years, interpretability research has emerged as a fundamental effort to ensure the development of robust and transparent AI systems aligned with human needs. This talk will focus on user-centric interpretability applications aimed at improving our understanding of machine translation systems, with the ultimate goal of improving post-editing productivity and enjoyability.

Towards User-centric Interpretability of NLP Models

With the astounding advances of artificial intelligence in recent years, the field of interpretability research has emerged as a fundamental effort to ensure the development of robust AI systems aligned with human values. In this talk, two perspectives on AI interpretability will be presented alongside two case studies in natural language processing. The first study leverages behavioral data and probing tasks to study the perception and encoding of linguistic complexity in humans and language models. The second introduces a user-centric interpretability perspective for neural machine translation to improve post-editing productivity and enjoyability. The need for such application-driven approaches will be emphasized in light of current challenges in faithfully evaluating advances in this field of study.

Empowering Human Translators via Interpretable Interactive Neural Machine Translation

Discussing the potential applications of interpretability research to the field of neural machine translation.

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