| Evaluating explainable AI: Which algorithmic explanations help users predict model behavior? P Hase, M Bansal arXiv preprint arXiv:2005.01831, 2020 | 223 | 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 | 88 | 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 | 61 | 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 | 56 | 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 | 55 | 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 | 49 | 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 | 48 | 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 | 47* | 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 | 41 | 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 | 17 | 2023 |
| Summarization programs: Interpretable abstractive summarization with neural modular trees S Saha, S Zhang, P Hase, M Bansal arXiv preprint arXiv:2209.10492, 2022 | 10 | 2022 |
| Low-cost algorithmic recourse for users with uncertain cost functions P Yadav, P Hase, M Bansal arXiv preprint arXiv:2111.01235, 2021 | 10 | 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 | 6* | 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 | 5 | 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 | 5 | 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 | 4 | 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 | | 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 | | 2023 |
Mohit BansalParker Professor, Computer Science, UNC Chapel HillE-mailová adresa ověřena na: cs.unc.edu
