| QuAC: Question answering in context E Choi, H He, M Iyyer, M Yatskar, W Yih, Y Choi, P Liang, L Zettlemoyer arXiv preprint arXiv:1808.07036, 2018 | 1087 | 2018 |
| Solving olympiad geometry without human demonstrations TH Trinh, Y Wu, QV Le, H He, T Luong Nature 625 (7995), 476-482, 2024 | 830 | 2024 |
| Delete, retrieve, generate: a simple approach to sentiment and style transfer J Li, R Jia, H He, P Liang arXiv preprint arXiv:1804.06437, 2018 | 703 | 2018 |
| FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization E Durmus, H He, M Diab arXiv preprint arXiv:2005.03754, 2020 | 532 | 2020 |
| Opponent modeling in deep reinforcement learning H He, J Boyd-Graber, K Kwok, H Daumé III International conference on machine learning, 1804-1813, 2016 | 457 | 2016 |
| Sharp nearby, fuzzy far away: How neural language models use context U Khandelwal, H He, P Qi, D Jurafsky arXiv preprint arXiv:1805.04623, 2018 | 427 | 2018 |
| Language models are greedy reasoners: A systematic formal analysis of chain-of-thought A Saparov, H He arXiv preprint arXiv:2210.01240, 2022 | 425 | 2022 |
| Single image super-resolution using Gaussian process regression H He, WC Siu CVPR 2011, 449-456, 2011 | 416 | 2011 |
| Learning to search in branch and bound algorithms H He, H Daumé, J Eisner Advances in neural information processing systems 27, 2014 | 381 | 2014 |
| Foundational challenges in assuring alignment and safety of large language models U Anwar, A Saparov, J Rando, D Paleka, M Turpin, P Hase, ES Lubana, ... arXiv preprint arXiv:2404.09932, 2024 | 296 | 2024 |
| Gluoncv and gluonnlp: Deep learning in computer vision and natural language processing J Guo, H He, T He, L Lausen, M Li, H Lin, X Shi, C Wang, J Xie, S Zha, ... Journal of Machine Learning Research 21 (23), 1-7, 2020 | 288 | 2020 |
| Iterative reasoning preference optimization RY Pang, W Yuan, H He, K Cho, S Sukhbaatar, J Weston Advances in Neural Information Processing Systems 37, 116617-116637, 2024 | 280 | 2024 |
| Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings H He, A Balakrishnan, M Eric, P Liang Association for Computational Linguistics (ACL), 2017 | 277 | 2017 |
| Decoupling strategy and generation in negotiation dialogues H He, D Chen, A Balakrishnan, P Liang arXiv preprint arXiv:1808.09637, 2018 | 248 | 2018 |
| Unlearn dataset bias in natural language inference by fitting the residual H He, S Zha, H Wang arXiv preprint arXiv:1908.10763, 2019 | 236 | 2019 |
| The PRISM alignment dataset: What participatory, representative and individualised human feedback reveals about the subjective and multicultural alignment of large language models HR Kirk, A Whitefield, P Rottger, AM Bean, K Margatina, ... Advances in Neural Information Processing Systems 37, 105236-105344, 2024 | 226 | 2024 |
| An empirical study on robustness to spurious correlations using pre-trained language models L Tu, G Lalwani, S Gella, H He Transactions of the Association for Computational Linguistics 8, 621-633, 2020 | 223 | 2020 |
| QuALITY: Question Answering with Long Input Texts, Yes! SR Bowman, A Chen, H He, N Joshi, J Ma, N Nangia, V Padmakumar, ... NAACL 2022, 2022 | 213* | 2022 |
| Meta-learning via language model in-context tuning Y Chen, R Zhong, S Zha, G Karypis, H He Proceedings of the 60th Annual Meeting of the Association for Computational …, 2022 | 196 | 2022 |
| Does writing with language models reduce content diversity? V Padmakumar, H He arXiv preprint arXiv:2309.05196, 2023 | 180 | 2023 |
Hal Daumé IIIAssociate Professor of Computer Science, University of MarylandVerified email at umiacs.umd.edu
