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He He 0001
Person information
- affiliation: New York University, NY, USA
- affiliation (former): Amazon Web Services, Palo Alto, CA, USA
- affiliation (former): Stanford University, CA, USA
- affiliation (Ph.D): University of Maryland, Department of Computer Science, College Park, MD, USA
Other persons with the same name
- He He — disambiguation page
- He He 0002 — State Grid Corporation of China, Shanghai, China
- He He 0003 — Systems Engineering Research Institute of CSSC, Beijing, China
- He He 0004
— Huazhong University of Science and Technology, Wuhan, China
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2020 – today
- 2024
- [j4]Aahlad Manas Puli, Nitish Joshi, Yoav Wald, He He, Rajesh Ranganath:
Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation. Trans. Mach. Learn. Res. 2024 (2024) - [j3]Sonia Castelo, João Rulff
: Visualization of AI-Assisted Task Guidance in AR. IEEE Trans. Vis. Comput. Graph. 30(1): 1313-1323 (2024) - [c53]Guande Wu, Chen Zhao, Cláudio T. Silva, He He:
Your Co-Workers Matter: Evaluating Collaborative Capabilities of Language Models in Blocks World. ACL (Findings) 2024: 4941-4957 - [c52]Yanda Chen, Chen Zhao, Zhou Yu, Kathleen R. McKeown, He He:
Parallel Structures in Pre-training Data Yield In-Context Learning. ACL (1) 2024: 8582-8592 - [c51]Richard Yuanzhe Pang, Stephen Roller, Kyunghyun Cho, He He, Jason Weston:
Leveraging Implicit Feedback from Deployment Data in Dialogue. EACL (2) 2024: 60-75 - [c50]Nitish Joshi, Javier Rando, Abulhair Saparov, Najoung Kim, He He:
Personas as a Way to Model Truthfulness in Language Models. EMNLP 2024: 6346-6359 - [c49]Nitish Joshi, Abulhair Saparov, Yixin Wang, He He:
LLMs Are Prone to Fallacies in Causal Inference. EMNLP 2024: 10553-10569 - [c48]Vishakh Padmakumar, He He:
Does Writing with Language Models Reduce Content Diversity? ICLR 2024 - [c47]Yanda Chen, Ruiqi Zhong, Narutatsu Ri, Chen Zhao, He He, Jacob Steinhardt, Zhou Yu, Kathleen R. McKeown:
Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations. ICML 2024 - [c46]Nicholas Lourie, Kyunghyun Cho, He He:
Show Your Work with Confidence: Confidence Bands for Tuning Curves. NAACL-HLT 2024: 3455-3472 - [i48]Yanda Chen, Chandan Singh, Xiaodong Liu, Simiao Zuo, Bin Yu, He He, Jianfeng Gao:
Towards Consistent Natural-Language Explanations via Explanation-Consistency Finetuning. CoRR abs/2401.13986 (2024) - [i47]Yanda Chen, Chen Zhao, Zhou Yu, Kathleen R. McKeown, He He:
Parallel Structures in Pre-training Data Yield In-Context Learning. CoRR abs/2402.12530 (2024) - [i46]Guande Wu, Chen Zhao, Cláudio T. Silva, He He:
Your Co-Workers Matter: Evaluating Collaborative Capabilities of Language Models in Blocks World. CoRR abs/2404.00246 (2024) - [i45]Usman Anwar, Abulhair Saparov, Javier Rando, Daniel Paleka, Miles Turpin, Peter Hase, Ekdeep Singh Lubana, Erik Jenner, Stephen Casper, Oliver Sourbut, Benjamin L. Edelman, Zhaowei Zhang, Mario Günther, Anton Korinek, José Hernández-Orallo, Lewis Hammond, Eric J. Bigelow, Alexander Pan, Lauro Langosco, Tomasz Korbak, Heidi Zhang, Ruiqi Zhong, Seán Ó hÉigeartaigh, Gabriel Recchia, Giulio Corsi, Alan Chan, Markus Anderljung, Lilian Edwards, Yoshua Bengio, Danqi Chen, Samuel Albanie, Tegan Maharaj, Jakob N. Foerster, Florian Tramèr, He He, Atoosa Kasirzadeh, Yejin Choi, David Krueger:
Foundational Challenges in Assuring Alignment and Safety of Large Language Models. CoRR abs/2404.09932 (2024) - [i44]Richard Yuanzhe Pang, Weizhe Yuan, Kyunghyun Cho, He He, Sainbayar Sukhbaatar, Jason Weston:
Iterative Reasoning Preference Optimization. CoRR abs/2404.19733 (2024) - [i43]Nitish Joshi, Abulhair Saparov, Yixin Wang, He He:
LLMs Are Prone to Fallacies in Causal Inference. CoRR abs/2406.12158 (2024) - [i42]Jane Pan, He He, Samuel R. Bowman, Shi Feng:
Spontaneous Reward Hacking in Iterative Self-Refinement. CoRR abs/2407.04549 (2024) - [i41]Jiaxin Wen, Ruiqi Zhong, Akbir Khan, Ethan Perez, Jacob Steinhardt, Minlie Huang, Samuel R. Bowman, He He, Shi Feng:
Language Models Learn to Mislead Humans via RLHF. CoRR abs/2409.12822 (2024) - 2023
- [c45]Richard Yuanzhe Pang, Vishakh Padmakumar, Thibault Sellam, Ankur P. Parikh, He He:
Reward Gaming in Conditional Text Generation. ACL (1) 2023: 4746-4763 - [c44]Chenghao Yang, Fan Yin, He He, Kai-Wei Chang, Xiaofei Ma, Bing Xiang:
Efficient Shapley Values Estimation by Amortization for Text Classification. ACL (1) 2023: 8666-8680 - [c43]Chenglei Si, Dan Friedman, Nitish Joshi, Shi Feng, Danqi Chen, He He:
Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations. ACL (1) 2023: 11289-11310 - [c42]Yanda Chen, Chen Zhao, Zhou Yu, Kathleen R. McKeown, He He:
On the Relation between Sensitivity and Accuracy in In-Context Learning. EMNLP (Findings) 2023: 155-167 - [c41]Abulhair Saparov, He He:
Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought. ICLR 2023 - [c40]Vishakh Padmakumar, Richard Yuanzhe Pang, He He, Ankur P. Parikh:
Extrapolative Controlled Sequence Generation via Iterative Refinement. ICML 2023: 26792-26808 - [c39]Abulhair Saparov, Richard Yuanzhe Pang, Vishakh Padmakumar, Nitish Joshi, Mehran Kazemi, Najoung Kim, He He:
Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples. NeurIPS 2023 - [i40]Vishakh Padmakumar, Richard Yuanzhe Pang, He He, Ankur P. Parikh:
Extrapolative Controlled Sequence Generation via Iterative Refinement. CoRR abs/2303.04562 (2023) - [i39]Chenglei Si, Dan Friedman, Nitish Joshi, Shi Feng, Danqi Chen, He He:
Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations. CoRR abs/2305.13299 (2023) - [i38]Abulhair Saparov, Richard Yuanzhe Pang, Vishakh Padmakumar, Nitish Joshi, Seyed Mehran Kazemi, Najoung Kim, He He:
Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples. CoRR abs/2305.15269 (2023) - [i37]Chenghao Yang, Fan Yin, He He, Kai-Wei Chang, Xiaofei Ma, Bing Xiang:
Efficient Shapley Values Estimation by Amortization for Text Classification. CoRR abs/2305.19998 (2023) - [i36]Yanda Chen, Ruiqi Zhong, Narutatsu Ri, Chen Zhao, He He, Jacob Steinhardt, Zhou Yu, Kathleen R. McKeown:
Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations. CoRR abs/2307.08678 (2023) - [i35]Richard Yuanzhe Pang, Stephen Roller, Kyunghyun Cho, He He, Jason Weston:
Leveraging Implicit Feedback from Deployment Data in Dialogue. CoRR abs/2307.14117 (2023) - [i34]Sonia Castelo, João Rulff, Erin McGowan, Bea Steers, Guande Wu, Shaoyu Chen, Irán R. Román, Roque Lopez, Ethan Brewer, Chen Zhao, Jing Qian, Kyunghyun Cho, He He, Qi Sun, Huy T. Vo, Juan Pablo Bello
ARGUS: Visualization of AI-Assisted Task Guidance in AR. CoRR abs/2308.06246 (2023) - [i33]Vishakh Padmakumar, He He:
Does Writing with Language Models Reduce Content Diversity? CoRR abs/2309.05196 (2023) - [i32]Nitish Joshi, Javier Rando, Abulhair Saparov, Najoung Kim, He He:
Personas as a Way to Model Truthfulness in Language Models. CoRR abs/2310.18168 (2023) - [i31]Nicholas Lourie, Kyunghyun Cho, He He:
Show Your Work with Confidence: Confidence Bands for Tuning Curves. CoRR abs/2311.09480 (2023) - 2022
- [c38]Yanda Chen, Ruiqi Zhong, Sheng Zha, George Karypis, He He:
Meta-learning via Language Model In-context Tuning. ACL (1) 2022: 719-730 - [c37]Faisal Ladhak, Esin Durmus, He He, Claire Cardie, Kathleen R. McKeown:
Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization. ACL (1) 2022: 1410-1421 - [c36]Nitish Joshi, He He:
An Investigation of the (In)effectiveness of Counterfactually Augmented Data. ACL (1) 2022: 3668-3681 - [c35]Tuhin Chakrabarty, Vishakh Padmakumar, He He:
Help me write a Poem - Instruction Tuning as a Vehicle for Collaborative Poetry Writing. EMNLP 2022: 6848-6863 - [c34]Nitish Joshi, Xiang Pan, He He:
Are All Spurious Features in Natural Language Alike? An Analysis through a Causal Lens. EMNLP 2022: 9804-9817 - [c33]Tianshu Wang, Faisal Ladhak, Esin Durmus, He He:
Improving Faithfulness by Augmenting Negative Summaries from Fake Documents. EMNLP 2022: 11913-11921 - [c32]Vishakh Padmakumar, He He:
Machine-in-the-Loop Rewriting for Creative Image Captioning. NAACL-HLT 2022: 573-586 - [c31]Vishakh Padmakumar, Leonard Lausen, Miguel Ballesteros, Sheng Zha, He He, George Karypis:
Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning. NAACL-HLT 2022: 2542-2550 - [c30]Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, Nikita Nangia, Jason Phang, Angelica Chen, Vishakh Padmakumar, Johnny Ma, Jana Thompson, He He, Samuel R. Bowman:
QuALITY: Question Answering with Long Input Texts, Yes! NAACL-HLT 2022: 5336-5358 - [e2]Spandana Gella, He He, Bodhisattwa Prasad Majumder, Burcu Can, Eleonora Giunchiglia, Samuel Cahyawijaya, Sewon Min, Maximilian Mozes, Xiang Lorraine Li, Isabelle Augenstein, Anna Rogers, Kyunghyun Cho, Edward Grefenstette, Laura Rimell, Chris Dyer:
Proceedings of the 7th Workshop on Representation Learning for NLP, RepL4NLP@ACL 2022, Dublin, Ireland, May 26, 2022. Association for Computational Linguistics 2022, ISBN 978-1-955917-48-3 [contents] - [i30]Vishakh Padmakumar, Leonard Lausen, Miguel Ballesteros, Sheng Zha, He He, George Karypis:
Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning. CoRR abs/2204.11117 (2022) - [i29]Yanda Chen, Chen Zhao, Zhou Yu, Kathleen R. McKeown, He He:
On the Relation between Sensitivity and Accuracy in In-context Learning. CoRR abs/2209.07661 (2022) - [i28]Abulhair Saparov, He He:
Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought. CoRR abs/2210.01240 (2022) - [i27]Aahlad Manas Puli, Nitish Joshi, He He, Rajesh Ranganath:
Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation. CoRR abs/2210.01302 (2022) - [i26]Tuhin Chakrabarty, Vishakh Padmakumar, He He:
Help me write a poem: Instruction Tuning as a Vehicle for Collaborative Poetry Writing. CoRR abs/2210.13669 (2022) - [i25]Nitish Joshi, Xiang Pan, He He:
Are All Spurious Features in Natural Language Alike? An Analysis through a Causal Lens. CoRR abs/2210.14011 (2022) - [i24]Richard Yuanzhe Pang, Vishakh Padmakumar, Thibault Sellam, Ankur P. Parikh, He He:
Reward Gaming in Conditional Text Generation. CoRR abs/2211.08714 (2022) - 2021
- [c29]Vishakh Padmakumar, He He:
Unsupervised Extractive Summarization using Pointwise Mutual Information. EACL 2021: 2505-2512 - [c28]Richard Yuanzhe Pang, He He:
Text Generation by Learning from Demonstrations. ICLR 2021 - [c27]Yana Dranker, He He, Yonatan Belinkov:
IRM - when it works and when it doesn't: A test case of natural language inference. NeurIPS 2021: 18212-18224 - [i23]Vishakh Padmakumar, He He:
Unsupervised Extractive Summarization using Pointwise Mutual Information. CoRR abs/2102.06272 (2021) - [i22]Nitish Joshi, He He:
An Investigation of the (In)effectiveness of Counterfactually Augmented Data. CoRR abs/2107.00753 (2021) - [i21]Faisal Ladhak, Esin Durmus, He He, Claire Cardie, Kathleen R. McKeown:
Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization. CoRR abs/2108.13684 (2021) - [i20]Yanda Chen, Ruiqi Zhong, Sheng Zha, George Karypis, He He:
Meta-learning via Language Model In-context Tuning. CoRR abs/2110.07814 (2021) - [i19]Vishakh Padmakumar, He He:
Machine-in-the-Loop Rewriting for Creative Image Captioning. CoRR abs/2111.04193 (2021) - [i18]Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, Nikita Nangia, Jason Phang, Angelica Chen, Vishakh Padmakumar, Johnny Ma, Jana Thompson, He He, Samuel R. Bowman:
QuALITY: Question Answering with Long Input Texts, Yes! CoRR abs/2112.08608 (2021) - [i17]Richard Yuanzhe Pang, He He, Kyunghyun Cho:
Amortized Noisy Channel Neural Machine Translation. CoRR abs/2112.08670 (2021) - 2020
- [j2]Jian Guo, He He, Tong He, Leonard Lausen, Mu Li, Haibin Lin, Xingjian Shi, Chenguang Wang, Junyuan Xie, Sheng Zha, Aston Zhang, Hang Zhang, Zhi Zhang, Zhongyue Zhang, Shuai Zheng, Yi Zhu:
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing. J. Mach. Learn. Res. 21: 23:1-23:7 (2020) - [j1]Lifu Tu, Garima Lalwani, Spandana Gella, He He:
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models. Trans. Assoc. Comput. Linguistics 8: 621-633 (2020) - [c26]Esin Durmus, He He, Mona T. Diab:
FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization. ACL 2020: 5055-5070 - [i16]Esin Durmus, He He, Mona T. Diab:
FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization. CoRR abs/2005.03754 (2020) - [i15]Lifu Tu, Garima Lalwani, Spandana Gella, He He:
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models. CoRR abs/2007.06778 (2020) - [i14]Richard Yuanzhe Pang, He He:
Text Generation by Learning from Off-Policy Demonstrations. CoRR abs/2009.07839 (2020)
2010 – 2019
- 2019
- [c25]He He, Sheng Zha, Haohan Wang:
Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual. DeepLo@EMNLP-IJCNLP 2019: 132-142 - [c24]Haibin Lin, Xingjian Shi, Leonard Lausen, Aston Zhang, He He, Sheng Zha, Alexander J. Smola:
Dive into Deep Learning for Natural Language Processing. EMNLP/IJCNLP (2) 2019 - [c23]He He, Nanyun Peng, Percy Liang:
Pun Generation with Surprise. NAACL-HLT (1) 2019: 1734-1744 - [c22]Yiheng Zhou, He He, Alan W. Black, Yulia Tsvetkov:
A Dynamic Strategy Coach for Effective Negotiation. SIGdial 2019: 367-378 - [i13]Pedro Rodriguez
Quizbowl: The Case for Incremental Question Answering. CoRR abs/1904.04792 (2019) - [i12]He He, Nanyun Peng, Percy Liang:
Pun Generation with Surprise. CoRR abs/1904.06828 (2019) - [i11]Jian Guo, He He, Tong He, Leonard Lausen, Mu Li, Haibin Lin, Xingjian Shi, Chenguang Wang, Junyuan Xie, Sheng Zha, Aston Zhang, Hang Zhang, Zhi Zhang, Zhongyue Zhang, Shuai Zheng
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing. CoRR abs/1907.04433 (2019) - [i10]He He, Sheng Zha, Haohan Wang:
Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual. CoRR abs/1908.10763 (2019) - [i9]Yiheng Zhou, He He, Alan W. Black, Yulia Tsvetkov:
A Dynamic Strategy Coach for Effective Negotiation. CoRR abs/1909.13426 (2019) - 2018
- [c21]Urvashi Khandelwal, He He, Peng Qi, Dan Jurafsky:
Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context. ACL (1) 2018: 284-294 - [c20]Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, Luke Zettlemoyer:
QuAC: Question Answering in Context. EMNLP 2018: 2174-2184 - [c19]He He, Derek Chen, Anusha Balakrishnan, Percy Liang:
Decoupling Strategy and Generation in Negotiation Dialogues. EMNLP 2018: 2333-2343 - [c18]Juncen Li, Robin Jia, He He, Percy Liang:
Delete, Retrieve, Generate: a Simple Approach to Sentiment and Style Transfer. NAACL-HLT 2018: 1865-1874 - [e1]Isabelle Augenstein, Kris Cao, He He, Felix Hill, Spandana Gella, Jamie Kiros, Hongyuan Mei, Dipendra Misra:
Proceedings of The Third Workshop on Representation Learning for NLP, Rep4NLP@ACL 2018, Melbourne, Australia, July 20, 2018. Association for Computational Linguistics 2018, ISBN 978-1-948087-43-8 [contents] - [i8]Juncen Li, Robin Jia, He He, Percy Liang:
Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer. CoRR abs/1804.06437 (2018) - [i7]Urvashi Khandelwal, He He, Peng Qi, Dan Jurafsky:
Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context. CoRR abs/1805.04623 (2018) - [i6]Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, Luke Zettlemoyer:
QuAC : Question Answering in Context. CoRR abs/1808.07036 (2018) - [i5]He He, Derek Chen, Anusha Balakrishnan, Percy Liang:
Decoupling Strategy and Generation in Negotiation Dialogues. CoRR abs/1808.09637 (2018) - 2017
- [c17]He He, Anusha Balakrishnan, Mihail Eric, Percy Liang:
Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings. ACL (1) 2017: 1766-1776 - [i4]He He, Anusha Balakrishnan, Mihail Eric, Percy Liang:
Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings. CoRR abs/1704.07130 (2017) - 2016
- [b1]He He:
Sequential Decisions and Predictions in Natural Language Processing. University of Maryland, College Park, MD, USA, 2016 - [c16]He He, Jordan L. Boyd-Graber:
Opponent Modeling in Deep Reinforcement Learning. ICML 2016: 1804-1813 - [c15]He He, Jordan L. Boyd-Graber, Hal Daumé III:
Interpretese vs. Translationese: The Uniqueness of Human Strategies in Simultaneous Interpretation. HLT-NAACL 2016: 971-976 - [c14]Kai-Wei Chang, He He, Stéphane Ross, Hal Daumé III, John Langford:
A Credit Assignment Compiler for Joint Prediction. NIPS 2016: 1705-1713 - [c13]Xi Stephen Chen, He He, Larry S. Davis:
Object detection in 20 questions. WACV 2016: 1-9 - [i3]He He, Paul Mineiro, Nikos Karampatziakis:
Active Information Acquisition. CoRR abs/1602.02181 (2016) - [i2]He He, Jordan L. Boyd-Graber, Kevin Kwok, Hal Daumé III:
Opponent Modeling in Deep Reinforcement Learning. CoRR abs/1609.05559 (2016) - 2015
- [c12]He He, Alvin Grissom II
Syntax-based Rewriting for Simultaneous Machine Translation. EMNLP 2015: 55-64 - [c11]Xiangyang Liu, He He, John S. Baras:
Crowdsourcing with multi-dimensional trust. FUSION 2015: 574-581 - [c10]Xiangyang Liu, He He, John S. Baras:
Trust-aware optimal crowdsourcing with budget constraint. ICC 2015: 1176-1181 - [c9]Hal Daumé III, John Langford, Kai-Wei Chang, He He, Sudha Rao:
Hands-on Learning to Search for Structured Prediction. HLT-NAACL 2015: 1 - [i1]Kai-Wei Chang, He He, Hal Daumé III, John Langford:
Learning to Search for Dependencies. CoRR abs/1503.05615 (2015) - 2014
- [c8]Alvin Grissom II, He He, Jordan L. Boyd-Graber, John Morgan, Hal Daumé III:
Don't Until the Final Verb Wait: Reinforcement Learning for Simultaneous Machine Translation. EMNLP 2014: 1342-1352 - [c7]He He, Hal Daumé III, Jason Eisner:
Learning to Search in Branch and Bound Algorithms. NIPS 2014: 3293-3301 - [c6]Lihong Li, He He, Jason D. Williams
Temporal supervised learning for inferring a dialog policy from example conversations. SLT 2014: 312-317 - 2013
- [c5]He He, Hal Daumé III, Jason Eisner:
Dynamic Feature Selection for Dependency Parsing. EMNLP 2013: 1455-1464 - 2012
- [c4]Jordan L. Boyd-Graber, Brianna Satinoff, He He, Hal Daumé III:
Besting the Quiz Master: Crowdsourcing Incremental Classification Games. EMNLP-CoNLL 2012: 1290-1301 - [c3]He He, Hal Daumé III, Jason Eisner:
Imitation Learning by Coaching. NIPS 2012: 3158-3166 - 2011
- [c2]He He, Wan-Chi Siu:
Single image super-resolution using Gaussian process regression. CVPR 2011: 449-456 - 2010
- [c1]He He, Ali Ghodsi:
Rare Class Classification by Support Vector Machine. ICPR 2010: 548-551
Coauthor Index
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