🔗 Who cites «Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?»

63 papers of our library cite it (of the 141,840 whose bibliographies we read) · 7 with a page here, the most cited first · ← back to «Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?»

1. Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving ModelsTongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, et al.2021 · 272 citations
2. Measuring Association Between Labels and Free-Text RationalesSarah Wiegreffe, Ana Marasović, Noah A. Smith2021 · 215 citations
3. Towards Faithful Model Explanation in NLP: A SurveyQing Lyu, Marianna Apidianaki, Chris Callison-Burch2024 · 195 citations
4. Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?Peter Hase, Shiyue Zhang, Harry Xie, et al.2020 · 120 citations
5. Aligning Faithful Interpretations with their Social AttributionAlon Jacovi, Yoav Goldberg2021 · 119 citations
6. Evaluating Explanations: How Much Do Explanations from the Teacher Aid Students?Danish Pruthi, Rachit Bansal, Bhuwan Dhingra, et al.2022 · 107 citations
7. PRover: Proof Generation for Interpretable Reasoning over RulesSwarnadeep Saha, Sayan Ghosh, Shashank Srivastava, et al.2020 · 101 citations

56 more papers of our library cite it; they have no page here yet.

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