Peter Hase
Evaluating explainable AI: Which algorithmic explanations help users predict model behavior?
P Hase, M Bansal
arXiv preprint arXiv:2005.01831, 2020
Interpretable image recognition with hierarchical prototypes
P Hase, C Chen, O Li, C Rudin
Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 7 …, 2019
Leakage-adjusted simulatability: Can models generate non-trivial explanations of their behavior in natural language?
P Hase, S Zhang, H Xie, M Bansal
arXiv preprint arXiv:2010.04119, 2020
Grips: Gradient-free, edit-based instruction search for prompting large language models
A Prasad, P Hase, X Zhou, M Bansal
arXiv preprint arXiv:2203.07281, 2022
Fastif: Scalable influence functions for efficient model interpretation and debugging
H Guo, NF Rajani, P Hase, M Bansal, C Xiong
arXiv preprint arXiv:2012.15781, 2020
When can models learn from explanations? a formal framework for understanding the roles of explanation data
P Hase, M Bansal
arXiv preprint arXiv:2102.02201, 2021
The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations
P Hase, H Xie, M Bansal
Advances in Neural Information Processing Systems 34, 2021
Do language models have beliefs? methods for detecting, updating, and visualizing model beliefs
P Hase, M Diab, A Celikyilmaz, X Li, Z Kozareva, V Stoyanov, M Bansal, ...
arXiv preprint arXiv:2111.13654, 2021
Open problems and fundamental limitations of reinforcement learning from human feedback
S Casper, X Davies, C Shi, TK Gilbert, J Scheurer, J Rando, R Freedman, ...
arXiv preprint arXiv:2307.15217, 2023
Does localization inform editing? surprising differences in causality-based localization vs. knowledge editing in language models
P Hase, M Bansal, B Kim, A Ghandeharioun
arXiv preprint arXiv:2301.04213, 2023
Summarization programs: Interpretable abstractive summarization with neural modular trees
S Saha, S Zhang, P Hase, M Bansal
arXiv preprint arXiv:2209.10492, 2022
Low-cost algorithmic recourse for users with uncertain cost functions
P Yadav, P Hase, M Bansal
arXiv preprint arXiv:2111.01235, 2021
Can Language Models Teach? Teacher Explanations Improve Student Performance via Personalization
S Saha, P Hase, M Bansal
Thirty-seventh Conference on Neural Information Processing Systems, 2023
Are hard examples also harder to explain? a study with human and model-generated explanations
S Saha, P Hase, N Rajani, M Bansal
arXiv preprint arXiv:2211.07517, 2022
Shall i compare thee to a machine-written sonnet? an approach to algorithmic sonnet generation
J Benhardt, P Hase, L Zhu, C Rudin
arXiv preprint arXiv:1811.05067, 2018
VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives
Z Ying, P Hase, M Bansal
Advances in Neural Information Processing Systems 35, 17057-17072, 2022
Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks
V Patil, P Hase, M Bansal
arXiv preprint arXiv:2309.17410, 2023
Adaptive Contextual Perception: How to Generalize to New Backgrounds and Ambiguous Objects
Z Ying, P Hase, M Bansal
arXiv preprint arXiv:2306.05963, 2023
Systém momentálně nemůže danou operaci provést. Zkuste to znovu později.
Články 1–18