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

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arxiv 2008.05122 v1 pith:FPUZKVCK submitted 2020-08-12 cs.CL

Ian Tenney , James Wexler , Jasmijn Bastings , Tolga Bolukbasi , Andy Coenen , Sebastian Gehrmann , Ellen Jiang , Mahima Pushkarna
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Carey Radebaugh Emily Reif Ann Yuan
This is my paper · ORCID
classification cs.CL
keywords analysisbehaviorexploringextensiblegenerationinterpretabilitylanguagelocal
verification ladder T0 review T1 audit T2 compute T3 formal
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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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