Peter Hase

Publication Activity (10 Years)

Years Active: 1997-2024
Publications (10 Years): 42

Top Topics

Multidatabase Transaction Management

Top Venues

Publications

Peter Hase, Mohit Bansal, Peter Clark, Sarah Wiegreffe
The Unreasonable Effectiveness of Easy Training Data for Hard Tasks. ACL (1) (2024)
Prateek Yadav, Peter Hase, Mohit Bansal
INSPIRE: Incorporating Diverse Feature Preferences in Recourse. Trans. Mach. Learn. Res. 2024 (2024)
Swarnadeep Saha, Archiki Prasad, Justin Chih-Yao Chen, Peter Hase, Elias Stengel-Eskin, Mohit Bansal
System-1.x: Learning to Balance Fast and Slow Planning with Language Models. CoRR (2024)
Peter Hase, Thomas Hofweber, Xiang Zhou, Elias Stengel-Eskin, Mohit Bansal
Fundamental Problems With Model Editing: How Should Rational Belief Revision Work in LLMs? CoRR (2024)
Elias Stengel-Eskin, Peter Hase, Mohit Bansal
LACIE: Listener-Aware Finetuning for Confidence Calibration in Large Language Models. CoRR (2024)
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 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
Foundational Challenges in Assuring Alignment and Safety of Large Language Models. CoRR (2024)
Thomas Hofweber, Peter Hase, Elias Stengel-Eskin, Mohit Bansal
Are language models rational? The case of coherence norms and belief revision. CoRR (2024)
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
Rethinking Machine Unlearning for Large Language Models. CoRR (2024)
Vaidehi Patil, Peter Hase, Mohit Bansal
Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks. ICLR (2024)
Peter Hase, Mohit Bansal, Peter Clark, Sarah Wiegreffe
The Unreasonable Effectiveness of Easy Training Data for Hard Tasks. CoRR (2024)
Vaidehi Patil, Peter Hase, Mohit Bansal
Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks. CoRR (2023)
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
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback. Trans. Mach. Learn. Res. 2023 (2023)
Swarnadeep Saha, Peter Hase, Mohit Bansal
Can Language Models Teach? Teacher Explanations Improve Student Performance via Personalization. NeurIPS (2023)
Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, Tony 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
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback. CoRR (2023)
Zhuofan Ying, Peter Hase, Mohit Bansal
Adaptive Contextual Perception: How To Generalize To New Backgrounds and Ambiguous Objects. NeurIPS (2023)
Peter Hase, Mona T. Diab, Asli Celikyilmaz, Xian Li, Zornitsa Kozareva, Veselin Stoyanov, Mohit Bansal, Srinivasan Iyer
Methods for Measuring, Updating, and Visualizing Factual Beliefs in Language Models. EACL (2023)
Swarnadeep Saha, Shiyue Zhang, Peter Hase, Mohit Bansal
Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees. ICLR (2023)
Peter Hase, Mohit Bansal, Been Kim, Asma Ghandeharioun
Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models. CoRR (2023)
Swarnadeep Saha, Peter Hase, Mohit Bansal
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Theory of Mind. CoRR (2023)
Peter Hase, Mohit Bansal, Been Kim, Asma Ghandeharioun
Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models. NeurIPS (2023)
Zhuofan Ying, Peter Hase, Mohit Bansal
Adaptive Contextual Perception: How to Generalize to New Backgrounds and Ambiguous Objects. CoRR (2023)
Archiki Prasad, Peter Hase, Xiang Zhou, Mohit Bansal
GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models. EACL (2023)
Archiki Prasad, Peter Hase, Xiang Zhou, Mohit Bansal
GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models. CoRR (2022)
Swarnadeep Saha, Shiyue Zhang, Peter Hase, Mohit Bansal
Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees. CoRR (2022)
Swarnadeep Saha, Peter Hase, Nazneen Rajani, Mohit Bansal
Are Hard Examples also Harder to Explain? A Study with Human and Model-Generated Explanations. EMNLP (2022)
Zhuofan Ying, Peter Hase, Mohit Bansal
VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives. CoRR (2022)
Swarnadeep Saha, Peter Hase, Nazneen Rajani, Mohit Bansal
Are Hard Examples also Harder to Explain? A Study with Human and Model-Generated Explanations. CoRR (2022)
Zhuofan Ying, Peter Hase, Mohit Bansal
VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives. NeurIPS (2022)
Peter Hase, Harry Xie, Mohit Bansal
Search Methods for Sufficient, Socially-Aligned Feature Importance Explanations with In-Distribution Counterfactuals. CoRR (2021)
Peter Hase, Harry Xie, Mohit Bansal
The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations. NeurIPS (2021)
Peter Hase, Mona T. Diab, Asli Celikyilmaz, Xian Li, Zornitsa Kozareva, Veselin Stoyanov, Mohit Bansal, Srinivasan Iyer
Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs. CoRR (2021)
Peter Hase, Mohit Bansal
When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data. CoRR (2021)
Prateek Yadav, Peter Hase, Mohit Bansal
Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions. CoRR (2021)
Han Guo, Nazneen Rajani, Peter Hase, Mohit Bansal, Caiming Xiong
FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging. EMNLP (1) (2021)
Han Guo, Nazneen Fatema Rajani, Peter Hase, Mohit Bansal, Caiming Xiong
FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging. CoRR (2020)
Peter Hase, Mohit Bansal
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? CoRR (2020)
Peter Hase, Shiyue Zhang, Harry Xie, Mohit Bansal
Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language? CoRR (2020)
Peter Hase, Mohit Bansal
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? ACL (2020)
Peter Hase, Shiyue Zhang, Harry Xie, Mohit Bansal
Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language? EMNLP (Findings) (2020)
Peter Hase, Chaofan Chen, Oscar Li, Cynthia Rudin
Interpretable Image Recognition with Hierarchical Prototypes. HCOMP (2019)
Peter Hase, Chaofan Chen, Oscar Li, Cynthia Rudin
Interpretable Image Recognition with Hierarchical Prototypes. CoRR (2019)
John Benhart, Tianlin Duan, Peter Hase, Liuyi Zhu, Cynthia Rudin
Shall I Compare Thee to a Machine-Written Sonnet? An Approach to Algorithmic Sonnet Generation. CoRR (2018)
Holger Husemann, Jörg Petersen, Christian Kanty, Hans-Dieter Kochs, Peter Hase
An User Adaptive Navigation Metaphor to Connect and Rate the Coherence of Terms and Complex Objects. Hypertext (1997)