Peter Hase
According to our database1,
Peter Hase
authored at least 24 papers
between 1997 and 2024.
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Bibliography
2024
2023
Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks.
CoRR, 2023
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback.
CoRR, 2023
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Theory of Mind.
CoRR, 2023
Adaptive Contextual Perception: How To Generalize To New Backgrounds and Ambiguous Objects.
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.
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.
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.
Proceedings of the Eleventh International Conference on Learning Representations, 2023
GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models.
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, 2023
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.
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.
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.
CoRR, 2021
Search Methods for Sufficient, Socially-Aligned Feature Importance Explanations with In-Distribution Counterfactuals.
CoRR, 2021
When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data.
CoRR, 2021
The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations.
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.
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?
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2020, 2020
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020
2019
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.
CoRR, 2018
1997
An User Adaptive Navigation Metaphor to Connect and Rate the Coherence of Terms and Complex Objects.
Proceedings of the Hypertext 97, 1997