Peter Hase
18 papers · 2020–2025 · 6 conferences · across top CS/AI conferences
Achievements
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π Academic Marathon (5) π Conference Polyglot (6) π Interdisciplinary Bridge π§ Keyword Pioneer π Cross-Pollinator (12)
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Renaissance Researcher
(5)
πΊοΈ
Taxonomy Completionist
(35)
π
Interdisciplinary Bridge
π€
Dynamic Duo
(18)
β
The Questioner
(7)
β‘
Prolific Year
(6)
ποΈ
Keyword Collector
(70)
π₯
Unstoppable
(6)
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Century Club
(18)
Conferences
NIPS (6)
ACL (3)
EMNLP (3)
ICLR (3)
EACL (2)
NAACL (1)
Top co-authors
Keywords
large language model
(4)
feature importance
(3)
preference optimization
(2)
in-context learning
(2)
knowledge editing
(2)
explanation generation
(2)
model simulatability
(2)
language model
(2)
question answering
(2)
natural language explanation
(2)
confidence calibration
(1)
visual question answering
(1)
domain generalization
(1)
explainable ai
(1)
prompt engineering
(1)
feature weighting
(1)
transfer learning
(1)
belief updating
(1)
out-of-distribution generalization
(1)
natural language generation
(1)
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
LACIE: Listener-Aware Finetuning for Calibration in Large Language Models
NIPS 2024
Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks
ICLR 2024
The Unreasonable Effectiveness of Easy Training Data for Hard Tasks
ACL 2024
Methods for Measuring, Updating, and Visualizing Factual Beliefs in Language Models
EACL 2023
Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models
NIPS 2023
Adaptive Contextual Perception: How To Generalize To New Backgrounds and Ambiguous Objects
NIPS 2023
Can Language Models Teach? Teacher Explanations Improve Student Performance via Personalization
NIPS 2023
GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models
EACL 2023
Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees
ICLR 2023
VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives
NIPS 2022
Are Hard Examples also Harder to Explain? A Study with Human and Model-Generated Explanations
EMNLP 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
NIPS 2021
Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?
EMNLP 2020
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?
ACL 2020