🧑‍🔬 Peter Hase — all works

33 works · ← Peter Hase

1. Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?Annual Meeting of the Association for Computational Linguistics (ACL)Peter Hase, Mohit Bansal2020 · 318 citations
2. Rethinking machine unlearning for large language models ↗Nature Machine Intelligence · no summary yetSijia Liu et al.2025 · 197 citations
3. GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language ModelsConference of the European Chapter of the Association for Computational Linguistics (EACL)Archiki Prasad, Peter Hase, Xiang Zhou, et al.2023 · 184 citations
4. Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models ↗arXiv:2301.04213 · no summary yetPeter Hase et al.2023 · 137 citations
5. Interpretable Image Recognition with Hierarchical Prototypes ↗Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · no summary yetPeter Hase et al.2019 · 131 citations
6. Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?Findings of the Association for Computational LinguisticsPeter Hase, Shiyue Zhang, Harry Xie, et al.2020 · 126 citations
7. Reasoning Models Don't Always Say What They Think ↗arXiv:2505.05410 · no summary yetYanda Chen et al.2025 · 106 citations
8. FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging ↗Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · no summary yetHan Guo et al.2021 · 86 citations
9. When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data ↗Proceedings of the First Workshop on Learning with Natural Language Supervision · no summary yetPeter Hase, Mohit Bansal2022 · 80 citations
10. Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs ↗arXiv:2111.13654 · no summary yetPeter Hase et al.2021 · 60 citations
11. Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks ↗arXiv (Cornell University) · no summary yetVaidehi Patil, Peter Hase, Mohit Bansal2023 · 56 citations
12. Methods for Measuring, Updating, and Visualizing Factual Beliefs in Language ModelsConference of the European Chapter of the Association for Computational Linguistics (EACL)Peter Hase, Mona Diab, Asli Celikyilmaz, et al.2023 · 51 citations
13. Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees ↗arXiv (Cornell University) · no summary yetSwarnadeep Saha et al.2022 · 23 citations
14. The Unreasonable Effectiveness of Easy Training Data for Hard Tasks ↗Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetPeter Hase et al.2024 · 21 citations
15. Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization ↗arXiv:2306.09299 · no summary yetSwarnadeep Saha, Peter Hase, Mohit Bansal2023 · 20 citations
16. The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations ↗arXiv (Cornell University) · no summary yetPeter Hase, Harry Xie, Mohit Bansal2021 · 18 citations
17. Are Hard Examples also Harder to Explain? A Study with Human and Model-Generated Explanations ↗Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · no summary yetSwarnadeep Saha et al.2022 · 17 citations
18. Fundamental Problems With Model Editing: How Should Rational Belief Revision Work in LLMs? ↗arXiv (Cornell University) · no summary yetPeter Hase et al.2024 · 15 citations
19. System-1.x: Learning to Balance Fast and Slow Planning with Language Models ↗arXiv (Cornell University) · no summary yetSwarnadeep Saha et al.2024 · 13 citations
20. LACIE: Listener-Aware Finetuning for Calibration in Large Language Models ↗neural information processing systems · no summary yetElias Stengel-Eskin, Peter Hase, Mohit Bansal2024 · 12 citations
21. LACIE: Listener-Aware Finetuning for Confidence Calibration in Large Language Models ↗arXiv (Cornell University) · no summary yetElias Stengel-Eskin, Peter Hase, Mohit Bansal2024 · 12 citations
22. VisFIS: Visual Feature Importance Supervision with Right-For-The-Right-Reason Objectives ↗neural information processing systems · no summary yetZhuofan Ying, Peter Hase, Mohit Bansal2022 · 11 citations
23. Teaching Models to Balance Resisting and Accepting Persuasion ↗Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · no summary yetElias Stengel-Eskin, Peter Hase, Mohit Bansal2025 · 9 citations
24. Can Language Models Teach? Teacher Explanations Improve Student Performance via Personalization ↗neural information processing systems · no summary yetSwarnadeep Saha, Peter Hase, Mohit Bansal2023 · 8 citations
25. Shall I Compare Thee to a Machine-Written Sonnet? An Approach to Algorithmic Sonnet Generation ↗arXiv (Cornell University) · no summary yetBenhardt, John et al.2018 · 7 citations
26. Are language models rational? The case of coherence norms and belief revision ↗arXiv:2406.03442 · no summary yetThomas Hofweber et al.2024 · 6 citations
27. Unlearning Sensitive Information in Multimodal LLMs: Benchmark and Attack-Defense Evaluation ↗arXiv (Cornell University) · no summary yetVaidehi Patil et al.2025 · 3 citations
28. Unsupervised Elicitation of Language Models ↗arXiv (Cornell University) · no summary yetJiaxin Wen et al.2025 · 2 citations
29. Sycophantic Agreement Transfers with Neutral Data via Contrastive Preference Optimization ↗arXiv (Cornell University) · no summary yetCamila Blank et al.2026 · 1 citation
30. Counterfactual Simulation Training for Chain-of-Thought Faithfulness ↗arXiv (Cornell University) · no summary yetPeter Hase, Christopher E. Potts2026 · 1 citation
31. Outcome Rewards Do Not Guarantee Verifiable or Causally Important Reasoning ↗arXiv (Cornell University) · no summary yetQinan Yu et al.2026 · 1 citation
32. Adaptive Contextual Perception: How To Generalize To New Backgrounds and Ambiguous Objects ↗neural information processing systems · no summary yetZhuofan Ying, Peter Hase, Mohit Bansal2023 · 1 citation
33. Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions ↗arXiv (Cornell University) · no summary yetPrateek Yadav, Peter Hase, Mohit Bansal2021

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