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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 predictionand 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. 1 * Equal contribution. 1 A video walkthrough is available at https://youtu. be/j0OfBWFUqIE.

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