Peter Hase

According to our database, Peter Hase authored at least 32 papers between 1997 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Interpretable and Controllable Language Models.

Peter Hase

PhD thesis, 2024

INSPIRE: Incorporating Diverse Feature Preferences in Recourse.

Prateek Yadav, Peter Hase, Mohit Bansal

Trans. Mach. Learn. Res., 2024

Teaching Models to Balance Resisting and Accepting Persuasion.

Elias Stengel-Eskin, Peter Hase, Mohit Bansal

CoRR, 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

CoRR, 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

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

LACIE: Listener-Aware Finetuning for Confidence Calibration in Large Language Models.

Elias Stengel-Eskin, Peter Hase, 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, 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

CoRR, 2024

Rethinking Machine Unlearning for Large Language Models.

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

CoRR, 2024

Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks.

Vaidehi Patil, Peter Hase, Mohit Bansal

Proceedings of the Twelfth International Conference on Learning Representations, 2024

The Unreasonable Effectiveness of Easy Training Data for Hard Tasks.

Peter Hase, Mohit Bansal, Peter Clark, Sarah Wiegreffe

Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024

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, 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

Trans. Mach. Learn. Res., 2023

Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Theory of Mind.

Swarnadeep Saha, Peter Hase, Mohit Bansal

CoRR, 2023

Adaptive Contextual Perception: How To Generalize To New Backgrounds and Ambiguous Objects.

Zhuofan Ying, Peter Hase, Mohit Bansal

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Can Language Models Teach? Teacher Explanations Improve Student Performance via Personalization.

Swarnadeep Saha, Peter Hase, Mohit Bansal

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees.

Swarnadeep Saha, Shiyue Zhang, Peter Hase, Mohit Bansal

Proceedings of the Eleventh International Conference on Learning Representations, 2023

GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models.

Archiki Prasad, Peter Hase, Xiang Zhou, Mohit Bansal

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, 2023

Methods for Measuring, Updating, and Visualizing Factual Beliefs in Language Models.

Peter Hase, Mona T. Diab, Asli Celikyilmaz, Xian Li, Zornitsa Kozareva, Veselin Stoyanov, Mohit Bansal, Srinivasan Iyer

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, 2023

2022

VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives.

Zhuofan Ying, Peter Hase, Mohit Bansal

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Are Hard Examples also Harder to Explain? A Study with Human and Model-Generated Explanations.

Swarnadeep Saha, Peter Hase, Nazneen Rajani, Mohit Bansal

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

2021

Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs.

Peter Hase, Mona T. Diab, Asli Celikyilmaz, Xian Li, Zornitsa Kozareva, Veselin Stoyanov, Mohit Bansal, Srinivasan Iyer

CoRR, 2021

Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions.

Prateek Yadav, 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

When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data.

Peter Hase, Mohit Bansal

CoRR, 2021

The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations.

Peter Hase, Harry Xie, Mohit Bansal

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging.

Han Guo, Nazneen Rajani, Peter Hase, Mohit Bansal, Caiming Xiong

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

2020

Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?

Peter Hase, Shiyue Zhang, Harry Xie, Mohit Bansal

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2020, 2020

Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?

Peter Hase, Mohit Bansal

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020

2019

Interpretable Image Recognition with Hierarchical Prototypes.

Peter Hase, Chaofan Chen, Oscar Li, Cynthia Rudin

Proceedings of the Seventh AAAI Conference on Human Computation and Crowdsourcing, 2019

2018

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

1997

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

Proceedings of the Hypertext 97, 1997