The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models

Ian Tenney; James Wexler; Jasmijn Bastings; Tolga Bolukbasi; Andy Coenen; Sebastian Gehrmann; Ellen Jiang; Mahima Pushkarna; Carey Radebaugh; Emily Reif; Ann Yuan

arXiv · 2008.05122

The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models

Ian Tenney·James Wexler·Jasmijn Bastings·Tolga Bolukbasi·Andy Coenen·Sebastian Gehrmann·Ellen Jiang·Mahima Pushkarna·Carey Radebaugh·Emily Reif·Ann Yuan

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Abstract

We present the Language Interpretability Tool (LIT), an open-source platform for visualization and understanding of NLP models. We focus on core questions about model behavior: Why did my model make this prediction? When does it perform poorly? What happens under a controlled change in the input? LIT integrates local explanations, aggregate analysis, and counterfactual generation into a streamlined, browser-based interface to enable rapid exploration and error analysis. We include case studies for a diverse set of workflows, including exploring counterfactuals for sentiment analysis, measuring gender bias in coreference systems, and exploring local behavior in text generation. LIT supports a wide range of models--including classification, seq2seq, and structured prediction--and is highly extensible through a declarative, framework-agnostic API. LIT is under active development, with code and full documentation available at https://github.com/pair-code/lit.

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BibTeX RIS

Ian Tenney, James Wexler, Jasmijn Bastings, Tolga Bolukbasi, Andy Coenen, Sebastian Gehrmann, Ellen Jiang, Mahima Pushkarna, Carey Radebaugh, Emily Reif, Ann Yuan. 2020-08-12. The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models. https://arxiv.org/abs/2008.05122

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