Peter Hase
18 papers · 2020–2025 · 6 venues · across top CS/AI venues
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large language model
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feature importance
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explanation generation
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question answering
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preference optimization
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knowledge editing
(2)
model editing
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in-context learning
(2)
language model
(2)
natural language explanation
(2)
model simulatability
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confidence calibration
(1)
visual question answering
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explainable ai
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domain generalization
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prompt engineering
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best-first search
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transfer learning
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Papers
System 1.x: Learning to Balance Fast and Slow Planning with Language Models
ICLR 2025
Teaching Models to Balance Resisting and Accepting Persuasion
NAACL 2025
Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks
ICLR 2024
LACIE: Listener-Aware Finetuning for Calibration in Large Language Models
NeurIPS 2024
The Unreasonable Effectiveness of Easy Training Data for Hard Tasks
ACL 2024
Adaptive Contextual Perception: How To Generalize To New Backgrounds and Ambiguous Objects
NeurIPS 2023
Can Language Models Teach? Teacher Explanations Improve Student Performance via Personalization
NeurIPS 2023
Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models
NeurIPS 2023
GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models
EACL 2023
Methods for Measuring, Updating, and Visualizing Factual Beliefs in Language Models
EACL 2023
Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees
ICLR 2023
Are Hard Examples also Harder to Explain? A Study with Human and Model-Generated Explanations
EMNLP 2022
VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives
NeurIPS 2022
When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data
ACL 2022
FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging
EMNLP 2021
The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations
NeurIPS 2021
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?
ACL 2020
Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?
EMNLP 2020