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arXiv · 2302.13942

Inseq: An Interpretability Toolkit for Sequence Generation Models

Gabriele Sarti·Nils Feldhus·Ludwig Sickert·Oskar van der Wal·Malvina Nissim·Arianna Bisazza
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Abstract

Past work in natural language processing interpretability focused mainly on popular classification tasks while largely overlooking generation settings, partly due to a lack of dedicated tools. In this work, we introduce Inseq, a Python library to democratize access to interpretability analyses of sequence generation models. Inseq enables intuitive and optimized extraction of models' internal information and feature importance scores for popular decoder-only and encoder-decoder Transformers architectures. We showcase its potential by adopting it to highlight gender biases in machine translation models and locate factual knowledge inside GPT-2. Thanks to its extensible interface supporting cutting-edge techniques such as contrastive feature attribution, Inseq can drive future advances in explainable natural language generation, centralizing good practices and enabling fair and reproducible model evaluations.

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Gabriele Sarti, Nils Feldhus, Ludwig Sickert, Oskar van der Wal, Malvina Nissim, Arianna Bisazza. 2023-02-27. Inseq: An Interpretability Toolkit for Sequence Generation Models. https://doi.org/10.18653/v1%2F2023.acl-demo.40

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