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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 . Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations 2020 – 58 citations

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Affective Computing Content Enrichment EMNLP Ethics & Fairness Has Code Interdisciplinary Approaches Interpretability RAG Tools Variational Autoencoders Visual Question Answering

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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