An User Adaptive Navigation Metaphor to Connect and Rate the Coherence of Terms and Complex Objects. - Holger Husemann; Jörg Petersen; Christian Kanty; Hans-Dieter Kochs; Peter Hase (Hypertext, 1997)

Shall I Compare Thee to a Machine-Written Sonnet? An Approach to Algorithmic Sonnet Generation. - John Benhart; Tianlin Duan; Peter Hase; Liuyi Zhu; Cynthia Rudin (CoRR, 2018)

Interpretable Image Recognition with Hierarchical Prototypes. - Peter Hase; Chaofan Chen; Oscar Li; Cynthia Rudin (HCOMP, 2019)

Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? - Peter Hase; Mohit Bansal (ACL, 2020)

Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language? - Peter Hase; Shiyue Zhang 001; Harry Xie; Mohit Bansal (EMNLP (Findings), 2020)

FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging. - Han Guo; Nazneen Rajani; Peter Hase; Mohit Bansal; Caiming Xiong (EMNLP (1), 2021)

The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations. - Peter Hase; Harry Xie; Mohit Bansal (NeurIPS, 2021)

When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data. - Peter Hase; Mohit Bansal (CoRR, 2021)

Search Methods for Sufficient, Socially-Aligned Feature Importance Explanations with In-Distribution Counterfactuals. - Peter Hase; Harry Xie; Mohit Bansal (CoRR, 2021)

Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions. - Prateek Yadav; Peter Hase; Mohit Bansal (CoRR, 2021)

Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs. - Peter Hase; Mona T. Diab; Asli Celikyilmaz; Xian Li 003; Zornitsa Kozareva; Veselin Stoyanov; Mohit Bansal; Srinivasan Iyer 001 (CoRR, 2021)

Are Hard Examples also Harder to Explain? A Study with Human and Model-Generated Explanations. - Swarnadeep Saha; Peter Hase; Nazneen Rajani; Mohit Bansal (EMNLP, 2022)

VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives. - Zhuofan Ying; Peter Hase; Mohit Bansal (NeurIPS, 2022)

Methods for Measuring, Updating, and Visualizing Factual Beliefs in Language Models. - Peter Hase; Mona T. Diab; Asli Celikyilmaz; Xian Li 003; Zornitsa Kozareva; Veselin Stoyanov; Mohit Bansal; Srinivasan Iyer 001 (EACL, 2023)

GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models. - Archiki Prasad; Peter Hase; Xiang Zhou; Mohit Bansal (EACL, 2023)

Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees. - Swarnadeep Saha; Shiyue Zhang 001; Peter Hase; Mohit Bansal (ICLR, 2023)

Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models. - Peter Hase; Mohit Bansal; Been Kim; Asma Ghandeharioun (NeurIPS, 2023)

Can Language Models Teach? Teacher Explanations Improve Student Performance via Personalization. - Swarnadeep Saha; Peter Hase; Mohit Bansal (NeurIPS, 2023)

Adaptive Contextual Perception: How To Generalize To New Backgrounds and Ambiguous Objects. - Zhuofan Ying; Peter Hase; Mohit Bansal (NeurIPS, 2023)

Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Theory of Mind. - Swarnadeep Saha; Peter Hase; Mohit Bansal (CoRR, 2023)

Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback. - Stephen Casper; Xander Davies; Claudia Shi; Thomas Krendl Gilbert; Jérémy Scheurer; Javier Rando; Rachel Freedman; Tomasz Korbak; David Lindner; Pedro Freire; Tony Tong Wang; Samuel Marks; et al. (Trans. Mach. Learn. Res., 2023)

The Unreasonable Effectiveness of Easy Training Data for Hard Tasks. - Peter Hase; Mohit Bansal; Peter Clark; Sarah Wiegreffe (ACL (1), 2024)

Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks. - Vaidehi Patil; Peter Hase; Mohit Bansal (ICLR, 2024)

LACIE: Listener-Aware Finetuning for Calibration in Large Language Models. - Elias Stengel-Eskin; Peter Hase; Mohit Bansal (NeurIPS, 2024)

Rethinking Machine Unlearning for Large Language Models. - Sijia Liu 001; Yuanshun Yao; Jinghan Jia; Stephen Casper; Nathalie Baracaldo; Peter Hase; Xiaojun Xu; Yuguang Yao; Hang Li 001; Kush R. Varshney; Mohit Bansal; Sanmi Koyejo; et al. (CoRR, 2024)

Foundational Challenges in Assuring Alignment and Safety of Large Language Models. - Usman Anwar; Abulhair Saparov; Javier Rando; Daniel Paleka; Miles Turpin; Peter Hase; Ekdeep Singh Lubana; Erik Jenner; Stephen Casper; Oliver Sourbut; Benjamin L. Edelman; Zhaowei Zhang; et al. (CoRR, 2024)

LACIE: Listener-Aware Finetuning for Confidence Calibration in Large Language Models. - Elias Stengel-Eskin; Peter Hase; Mohit Bansal (CoRR, 2024)

Are language models rational? The case of coherence norms and belief revision. - Thomas Hofweber; Peter Hase; Elias Stengel-Eskin; Mohit Bansal (CoRR, 2024)

Foundational Challenges in Assuring Alignment and Safety of Large Language Models. - Usman Anwar; Abulhair Saparov; Javier Rando; Daniel Paleka; Miles Turpin; Peter Hase; Ekdeep Singh Lubana; Erik Jenner; Stephen Casper; Oliver Sourbut; Benjamin L. Edelman; Zhaowei Zhang; et al. (Trans. Mach. Learn. Res., 2024)

Fundamental Problems With Model Editing: How Should Rational Belief Revision Work in LLMs? - Peter Hase; Thomas Hofweber; Xiang Zhou; Elias Stengel-Eskin; Mohit Bansal (Trans. Mach. Learn. Res., 2024)

Unlearning Sensitive Information in Multimodal LLMs: Benchmark and Attack-Defense Evaluation. - Vaidehi Patil; Yi-Lin Sung; Peter Hase; Jie Peng 002; Tianlong Chen 001; Mohit Bansal (Trans. Mach. Learn. Res., 2024)

INSPIRE: Incorporating Diverse Feature Preferences in Recourse. - Prateek Yadav; Peter Hase; Mohit Bansal (Trans. Mach. Learn. Res., 2024)

Interpretable and Controllable Language Models. - Peter Hase (2024)

System 1.x: Learning to Balance Fast and Slow Planning with Language Models. - Swarnadeep Saha; Archiki Prasad; Justin Chih-Yao Chen; Peter Hase; Elias Stengel-Eskin; Mohit Bansal (ICLR, 2025)

Teaching Models to Balance Resisting and Accepting Persuasion. - Elias Stengel-Eskin; Peter Hase; Mohit Bansal (NAACL (Long Papers), 2025)

Rethinking machine unlearning for large language models. - Sijia Liu 001; Yuanshun Yao; Jinghan Jia; Stephen Casper; Nathalie Baracaldo; Peter Hase; Yuguang Yao; Chris Yuhao Liu; Xiaojun Xu; Hang Li 001; Kush R. Varshney; Mohit Bansal; et al. (Nat. Mac. Intell., 2025)

Reasoning Models Don't Always Say What They Think. - Yanda Chen; Joe Benton; Ansh Radhakrishnan; Jonathan Uesato; Carson Denison; John Schulman; Arushi Somani; Peter Hase; Misha Wagner; Fabien Roger; Vladimir Mikulik; Samuel R. Bowman; et al. (CoRR, 2025)

Unsupervised Elicitation of Language Models. - Jiaxin Wen; Zachary Ankner; Arushi Somani; Peter Hase; Samuel Marks; Jacob Goldman-Wetzler; Linda Petrini; Henry Sleight; Collin Burns; He He 001; Shi Feng 005; Ethan Perez; et al. (CoRR, 2025)