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

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.
Anthology ID:
2020.emnlp-demos.15
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
October
Year:
2020
Address:
Online
Editors:
Qun Liu, David Schlangen
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
107–118
Language:
URL:
https://aclanthology.org/2020.emnlp-demos.15
DOI:
10.18653/v1/2020.emnlp-demos.15
Bibkey:
Cite (ACL):
Ian Tenney, James Wexler, Jasmijn Bastings, Tolga Bolukbasi, Andy Coenen, Sebastian Gehrmann, Ellen Jiang, Mahima Pushkarna, Carey Radebaugh, Emily Reif, and Ann Yuan. 2020. The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 107–118, Online. Association for Computational Linguistics.
Cite (Informal):
The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models (Tenney et al., EMNLP 2020)
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PDF:
https://preview.aclanthology.org/add_acl24_videos/2020.emnlp-demos.15.pdf
Code
 PAIR-code/lit
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