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2020 – today
- 2023
[c10]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: 2706-2723- [c9]Archiki Prasad, Peter Hase, Xiang Zhou, Mohit Bansal:
GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models. EACL 2023: 3827-3846 - [i14]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 abs/2301.04213 (2023) - 2022
- [c8]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: 2121-2131 - [c7]Zhuofan Ying, Peter Hase, Mohit Bansal:
VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives. NeurIPS 2022 - [i13]Archiki Prasad, Peter Hase, Xiang Zhou, Mohit Bansal:
GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models. CoRR abs/2203.07281 (2022) - [i12]Zhuofan Ying, Peter Hase, Mohit Bansal:
VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives. CoRR abs/2206.11212 (2022) - [i11]Swarnadeep Saha, Shiyue Zhang, Peter Hase, Mohit Bansal:
Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees. CoRR abs/2209.10492 (2022) - [i10]Swarnadeep Saha, Peter Hase, Nazneen Rajani, Mohit Bansal:
Are Hard Examples also Harder to Explain? A Study with Human and Model-Generated Explanations. CoRR abs/2211.07517 (2022) - 2021
- [c6]Han Guo, Nazneen Rajani, Peter Hase, Mohit Bansal, Caiming Xiong:
FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging. EMNLP (1) 2021: 10333-10350 - [c5]Peter Hase, Harry Xie, Mohit Bansal:
The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations. NeurIPS 2021: 3650-3666 - [i9]Peter Hase, Mohit Bansal:
When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data. CoRR abs/2102.02201 (2021) - [i8]Peter Hase, Harry Xie, Mohit Bansal:
Search Methods for Sufficient, Socially-Aligned Feature Importance Explanations with In-Distribution Counterfactuals. CoRR abs/2106.00786 (2021) - [i7]Prateek Yadav, Peter Hase, Mohit Bansal:
Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions. CoRR abs/2111.01235 (2021) - [i6]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 abs/2111.13654 (2021) - 2020
- [c4]Peter Hase, Mohit Bansal:
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? ACL 2020: 5540-5552 - [c3]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: 4351-4367 - [i5]Peter Hase, Mohit Bansal:
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? CoRR abs/2005.01831 (2020) - [i4]Peter Hase, Shiyue Zhang, Harry Xie, Mohit Bansal:
Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language? CoRR abs/2010.04119 (2020) - [i3]Han Guo, Nazneen Fatema Rajani, Peter Hase, Mohit Bansal, Caiming Xiong:
FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging. CoRR abs/2012.15781 (2020)
2010 – 2019
- 2019
- [c2]Peter Hase, Chaofan Chen, Oscar Li, Cynthia Rudin:
Interpretable Image Recognition with Hierarchical Prototypes. HCOMP 2019: 32-40 - [i2]Peter Hase, Chaofan Chen, Oscar Li, Cynthia Rudin:
Interpretable Image Recognition with Hierarchical Prototypes. CoRR abs/1906.10651 (2019) - 2018
- [i1]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 abs/1811.05067 (2018)
1990 – 1999
- 1997
- [c1]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: 214-215
Coauthor Index
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last updated on 2023-05-13 04:00 CEST by the dblp team
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