Peter Hase - RankMe

57 papers A* 11A 2B 1Journal 37Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Counterfactual Simulation Training for Chain-of-Thought Faithfulness. CoRR Peter Hase, Christopher Potts
2026	J	`jnl`	The Truthfulness Spectrum Hypothesis. CoRR Zhuofan Josh Ying, Shauli Ravfogel, Nikolaus Kriegeskorte, Peter Hase
2025	J	`jnl`	Reasoning Models Don't Always Say What They Think. CoRR 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, Jan Leike, Jared Kaplan, Ethan Perez
2025	J	`jnl`	Rethinking machine unlearning for large language models. Nat. Mac. Intell. Sijia Liu, Yuanshun Yao, Jinghan Jia, Stephen Casper, Nathalie Baracaldo, Peter Hase, Yuguang Yao, Chris Yuhao Liu, Xiaojun Xu, Hang Li, Kush R. Varshney, Mohit Bansal, Sanmi Koyejo, Yang Liu
2025	A*	`conf`	System 1.x: Learning to Balance Fast and Slow Planning with Language Models. ICLR Swarnadeep Saha, Archiki Prasad, Justin Chih-Yao Chen, Peter Hase, Elias Stengel-Eskin, Mohit Bansal
2025	—	`conf`	Teaching Models to Balance Resisting and Accepting Persuasion. NAACL (Long Papers) Elias Stengel-Eskin, Peter Hase, Mohit Bansal
2025	J	`jnl`	Unlearning Sensitive Information in Multimodal LLMs: Benchmark and Attack-Defense Evaluation. CoRR Vaidehi Patil, Yi-Lin Sung, Peter Hase, Jie Peng, Tianlong Chen, Mohit Bansal
2025	J	`jnl`	Unsupervised Elicitation of Language Models. CoRR Jiaxin Wen, Zachary Ankner, Arushi Somani, Peter Hase, Samuel Marks, Jacob Goldman-Wetzler, Linda Petrini, Henry Sleight, Collin Burns, He He, Shi Feng, Ethan Perez, Jan Leike
2024	J	`jnl`	Are language models rational? The case of coherence norms and belief revision. CoRR Thomas Hofweber, Peter Hase, Elias Stengel-Eskin, Mohit Bansal
2024	A*	`conf`	Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks. ICLR Vaidehi Patil, Peter Hase, Mohit Bansal
2024	J	`jnl`	Foundational Challenges in Assuring Alignment and Safety of Large Language Models. CoRR 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, Mario Günther, Anton Korinek, José Hernández-Orallo, Lewis Hammond, Eric J. Bigelow, Alexander Pan, Lauro Langosco, Tomasz Korbak, Heidi C. Zhang, Ruiqi Zhong, Seán Ó hÉigeartaigh, Gabriel Recchia, Giulio Corsi, Alan Chan, Markus Anderljung, Lilian Edwards, Yoshua Bengio, Danqi Chen, Samuel Albanie, Tegan Maharaj, Jakob N. Foerster, Florian Tramèr, He He, Atoosa Kasirzadeh, Yejin Choi, David Krueger
2024	J	`jnl`	Foundational Challenges in Assuring Alignment and Safety of Large Language Models. Trans. Mach. Learn. Res. 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, Mario Günther, Anton Korinek, José Hernández-Orallo, Lewis Hammond, Eric J. Bigelow, Alexander Pan, Lauro Langosco, Tomasz Korbak, Heidi Chenyu Zhang, Ruiqi Zhong, Seán Ó hÉigeartaigh, Gabriel Recchia, Giulio Corsi, Alan Chan, Markus Anderljung, Lilian Edwards, Aleksandar Petrov, Christian Schröder de Witt, Sumeet Ramesh Motwani, Yoshua Bengio, Danqi Chen, Philip Torr, Samuel Albanie, Tegan Maharaj, Jakob Nicolaus Foerster, Florian Tramèr, He He, Atoosa Kasirzadeh, Yejin Choi, David Krueger
2024	J	`jnl`	Fundamental Problems With Model Editing: How Should Rational Belief Revision Work in LLMs? CoRR Peter Hase, Thomas Hofweber, Xiang Zhou, Elias Stengel-Eskin, Mohit Bansal
2024	J	`jnl`	Fundamental Problems With Model Editing: How Should Rational Belief Revision Work in LLMs? Trans. Mach. Learn. Res. Peter Hase, Thomas Hofweber, Xiang Zhou, Elias Stengel-Eskin, Mohit Bansal
2024	J	`jnl`	INSPIRE: Incorporating Diverse Feature Preferences in Recourse. Trans. Mach. Learn. Res. Prateek Yadav, Peter Hase, Mohit Bansal
2024	—	`—`	Interpretable and Controllable Language Models. Peter Hase
2024	A*	`conf`	LACIE: Listener-Aware Finetuning for Calibration in Large Language Models. NeurIPS Elias Stengel-Eskin, Peter Hase, Mohit Bansal
2024	J	`jnl`	LACIE: Listener-Aware Finetuning for Confidence Calibration in Large Language Models. CoRR Elias Stengel-Eskin, Peter Hase, Mohit Bansal
2024	J	`jnl`	Rethinking Machine Unlearning for Large Language Models. CoRR Sijia Liu, Yuanshun Yao, Jinghan Jia, Stephen Casper, Nathalie Baracaldo, Peter Hase, Xiaojun Xu, Yuguang Yao, Hang Li, Kush R. Varshney, Mohit Bansal, Sanmi Koyejo, Yang Liu
2024	J	`jnl`	System-1.x: Learning to Balance Fast and Slow Planning with Language Models. CoRR Swarnadeep Saha, Archiki Prasad, Justin Chih-Yao Chen, Peter Hase, Elias Stengel-Eskin, Mohit Bansal
2024	J	`jnl`	Teaching Models to Balance Resisting and Accepting Persuasion. CoRR Elias Stengel-Eskin, Peter Hase, Mohit Bansal
2024	—	`conf`	The Unreasonable Effectiveness of Easy Training Data for Hard Tasks. ACL (1) Peter Hase, Mohit Bansal, Peter Clark, Sarah Wiegreffe
2024	J	`jnl`	The Unreasonable Effectiveness of Easy Training Data for Hard Tasks. CoRR Peter Hase, Mohit Bansal, Peter Clark, Sarah Wiegreffe
2024	J	`jnl`	Unlearning Sensitive Information in Multimodal LLMs: Benchmark and Attack-Defense Evaluation. Trans. Mach. Learn. Res. Vaidehi Patil, Yi-Lin Sung, Peter Hase, Jie Peng, Tianlong Chen, Mohit Bansal
2023	A*	`conf`	Adaptive Contextual Perception: How To Generalize To New Backgrounds and Ambiguous Objects. NeurIPS Zhuofan Ying, Peter Hase, Mohit Bansal
2023	J	`jnl`	Adaptive Contextual Perception: How to Generalize to New Backgrounds and Ambiguous Objects. CoRR Zhuofan Ying, Peter Hase, Mohit Bansal
2023	J	`jnl`	Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Theory of Mind. CoRR Swarnadeep Saha, Peter Hase, Mohit Bansal
2023	A*	`conf`	Can Language Models Teach? Teacher Explanations Improve Student Performance via Personalization. NeurIPS Swarnadeep Saha, Peter Hase, Mohit Bansal
2023	J	`jnl`	Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks. CoRR Vaidehi Patil, Peter Hase, Mohit Bansal
2023	A*	`conf`	Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models. NeurIPS Peter Hase, Mohit Bansal, Been Kim, Asma Ghandeharioun
2023	J	`jnl`	Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models. CoRR Peter Hase, Mohit Bansal, Been Kim, Asma Ghandeharioun
2023	A	`conf`	GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models. EACL Archiki Prasad, Peter Hase, Xiang Zhou, Mohit Bansal
2023	A	`conf`	Methods for Measuring, Updating, and Visualizing Factual Beliefs in Language Models. EACL Peter Hase, Mona T. Diab, Asli Celikyilmaz, Xian Li, Zornitsa Kozareva, Veselin Stoyanov, Mohit Bansal, Srinivasan Iyer
2023	J	`jnl`	Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback. CoRR 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, Charbel-Raphaël Ségerie, Micah Carroll, Andi Peng, Phillip J. K. Christoffersen, Mehul Damani, Stewart Slocum, Usman Anwar, Anand Siththaranjan, Max Nadeau, Eric J. Michaud, Jacob Pfau, Dmitrii Krasheninnikov, Xin Chen, Lauro Langosco, Peter Hase, Erdem Biyik, Anca D. Dragan, David Krueger, Dorsa Sadigh, Dylan Hadfield-Menell
2023	J	`jnl`	Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback. Trans. Mach. Learn. Res. 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, Charbel-Raphaël Ségerie, Micah Carroll, Andi Peng, Phillip J. K. Christoffersen, Mehul Damani, Stewart Slocum, Usman Anwar, Anand Siththaranjan, Max Nadeau, Eric J. Michaud, Jacob Pfau, Dmitrii Krasheninnikov, Xin Chen, Lauro Langosco, Peter Hase, Erdem Biyik, Anca D. Dragan, David Krueger, Dorsa Sadigh, Dylan Hadfield-Menell
2023	A*	`conf`	Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees. ICLR Swarnadeep Saha, Shiyue Zhang, Peter Hase, Mohit Bansal
2022	A*	`conf`	Are Hard Examples also Harder to Explain? A Study with Human and Model-Generated Explanations. EMNLP Swarnadeep Saha, Peter Hase, Nazneen Rajani, Mohit Bansal
2022	J	`jnl`	Are Hard Examples also Harder to Explain? A Study with Human and Model-Generated Explanations. CoRR Swarnadeep Saha, Peter Hase, Nazneen Rajani, Mohit Bansal
2022	J	`jnl`	GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models. CoRR Archiki Prasad, Peter Hase, Xiang Zhou, Mohit Bansal
2022	J	`jnl`	Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees. CoRR Swarnadeep Saha, Shiyue Zhang, Peter Hase, Mohit Bansal
2022	A*	`conf`	VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives. NeurIPS Zhuofan Ying, Peter Hase, Mohit Bansal
2022	J	`jnl`	VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives. CoRR Zhuofan Ying, Peter Hase, Mohit Bansal
2021	J	`jnl`	Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs. CoRR Peter Hase, Mona T. Diab, Asli Celikyilmaz, Xian Li, Zornitsa Kozareva, Veselin Stoyanov, Mohit Bansal, Srinivasan Iyer
2021	—	`conf`	FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging. EMNLP (1) Han Guo, Nazneen Rajani, Peter Hase, Mohit Bansal, Caiming Xiong
2021	J	`jnl`	Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions. CoRR Prateek Yadav, Peter Hase, Mohit Bansal
2021	J	`jnl`	Search Methods for Sufficient, Socially-Aligned Feature Importance Explanations with In-Distribution Counterfactuals. CoRR Peter Hase, Harry Xie, Mohit Bansal
2021	A*	`conf`	The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations. NeurIPS Peter Hase, Harry Xie, Mohit Bansal
2021	J	`jnl`	When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data. CoRR Peter Hase, Mohit Bansal
2020	A*	`conf`	Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? ACL Peter Hase, Mohit Bansal
2020	J	`jnl`	Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? CoRR Peter Hase, Mohit Bansal
2020	J	`jnl`	FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging. CoRR Han Guo, Nazneen Fatema Rajani, Peter Hase, Mohit Bansal, Caiming Xiong
2020	—	`conf`	Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language? EMNLP (Findings) Peter Hase, Shiyue Zhang, Harry Xie, Mohit Bansal
2020	J	`jnl`	Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language? CoRR Peter Hase, Shiyue Zhang, Harry Xie, Mohit Bansal
2019	B	`conf`	Interpretable Image Recognition with Hierarchical Prototypes. HCOMP Peter Hase, Chaofan Chen, Oscar Li, Cynthia Rudin
2019	J	`jnl`	Interpretable Image Recognition with Hierarchical Prototypes. CoRR Peter Hase, Chaofan Chen, Oscar Li, Cynthia Rudin
2018	J	`jnl`	Shall I Compare Thee to a Machine-Written Sonnet? An Approach to Algorithmic Sonnet Generation. CoRR John Benhart, Tianlin Duan, Peter Hase, Liuyi Zhu, Cynthia Rudin
1997	—	`conf`	An User Adaptive Navigation Metaphor to Connect and Rate the Coherence of Terms and Complex Objects. Hypertext Holger Husemann, Jörg Petersen, Christian Kanty, Hans-Dieter Kochs, Peter Hase