The Language Interpretability Tool (LIT) is for researchers and practitioners looking to understand NLP model behavior through a visual, interactive, and extensible tool.

Use LIT to ask and answer questions like:

• What kind of examples does my model perform poorly on?
• Why did my model make this prediction? Can it attribute it to adversarial behavior, or undesirable priors from the training set?
• Does my model behave consistently if I change things like textual style, verb tense, or pronoun gender?

LIT contains many built-in capabilities but is also customizable, with the ability to add custom interpretability techniques, metrics calculations, counterfactual generators, visualizations, and more.

LIT also contains preliminary support for non-language models, working with tabular and image data. For a similar tool built to explore general-purpose machine learning models, check out the What-If Tool.

LIT can be run as a standalone server, or inside of python notebook environments such as Colab, Jupyter, and Google Cloud Vertex AI Notebooks.