Theo dõi
He He
He He
New York University
Email được xác minh tại cs.nyu.edu - Trang chủ
Tiêu đề
Trích dẫn bởi
Trích dẫn bởi
Năm
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
9682018
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
6552018
FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization
E Durmus, H He, M Diab
arXiv preprint arXiv:2005.03754, 2020
4292020
Opponent modeling in deep reinforcement learning
H He, J Boyd-Graber, K Kwok, H Daumé III
International conference on machine learning, 1804-1813, 2016
4072016
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
3972018
Single image super-resolution using Gaussian process regression
H He, WC Siu
CVPR 2011, 449-456, 2011
3922011
Solving olympiad geometry without human demonstrations
TH Trinh, Y Wu, QV Le, H He, T Luong
Nature 625 (7995), 476-482, 2024
3792024
Learning to search in branch and bound algorithms
H He, H Daume III, JM Eisner
Advances in neural information processing systems 27, 2014
3252014
Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings
H He, A Balakrishnan, M Eric, P Liang
Association for Computational Linguistics (ACL), 2017
2612017
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
2502020
Language models are greedy reasoners: A systematic formal analysis of chain-of-thought
A Saparov, H He
arXiv preprint arXiv:2210.01240, 2022
2402022
Unlearn dataset bias in natural language inference by fitting the residual
H He, S Zha, H Wang
arXiv preprint arXiv:1908.10763, 2019
2152019
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
1932020
Decoupling strategy and generation in negotiation dialogues
H He, D Chen, A Balakrishnan, P Liang
arXiv preprint arXiv:1808.09637, 2018
1922018
Meta-learning via language model in-context tuning
Y Chen, R Zhong, S Zha, G Karypis, H He
arXiv preprint arXiv:2110.07814, 2021
1402021
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
1362024
Imitation learning by coaching
H He, J Eisner, H Daume
Advances in neural information processing systems 25, 2012
1322012
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
130*2022
Don’t until the final verb wait: Reinforcement learning for simultaneous machine translation
A Grissom II, H He, J Boyd-Graber, J Morgan, H Daumé III
Proceedings of the 2014 Conference on empirical methods in natural language …, 2014
1212014
Types of out-of-distribution texts and how to detect them
U Arora, W Huang, H He
arXiv preprint arXiv:2109.06827, 2021
1002021
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