🧑‍🔬 He He — all works

33 works · ← He He

1. QuAC: Question Answering in ContextConference on Empirical Methods in Natural Language Processing (EMNLP)Eunsol Choi, He He, Mohit Iyyer, et al.2018 · 1,066 citations
2. Solving olympiad geometry without human demonstrationsNatureTrieu H. Trinh, Yuhuai Wu, Quoc V. Le, et al.2024 · 498 citations
3. Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context ↗arXiv:1805.04623 · no summary yetUrvashi Khandelwal et al.2018 · 399 citations
4. FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive SummarizationAnnual Meeting of the Association for Computational Linguistics (ACL)Esin Durmus, He He, Mona Diab2020 · 359 citations
5. Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style TransferJuncen Li, Robin Jia, He He, et al.Machine translation · 351 citations
6. Unlearn Dataset Bias in Natural Language Inference by Fitting the ResidualWorkshop on Deep Learning Approaches for Low-Resource NLPHe He, Sheng Zha, Haohan Wang2019 · 260 citations
7. Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings ↗Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetHe He et al.2017 · 258 citations
8. Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-ThoughtarXiv:2210.01240Abulhair Saparov, He He2022 · 244 citations
9. Decoupling Strategy and Generation in Negotiation DialoguesConference on Empirical Methods in Natural Language Processing (EMNLP)He He, Derek Chen, Anusha Balakrishnan, et al.2018 · 236 citations
10. QuALITY: Question Answering with Long Input Texts, Yes!Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, et al.2022 · 167 citations
11. Meta-learning via Language Model In-context Tuning ↗arXiv:2110.07814 · no summary yetYanda Chen et al.2021 · 160 citations
12. Iterative Reasoning Preference OptimizationConference on Neural Information Processing Systems (NeurIPS)Richard Yuanzhe Pang, Weizhe Yuan, Kyunghyun Cho, et al.2024 · 153 citations
13. The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models ↗arXiv:2404.16019 · The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2024) · no summary yetHannah Rose Kirk et al.2024 · 133 citations
14. Pun Generation with Surprise ↗Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · no summary yetHe He, Nanyun Peng, Percy Liang2019 · 106 citations
15. Types of Out-of-Distribution Texts and How to Detect Them ↗Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · no summary yetUdit Arora, William Huang, He He2021 · 103 citations
16. Foundational Challenges in Assuring Alignment and Safety of Large Language Models ↗arXiv:2404.09932 · no summary yetUsman Anwar et al.2024 · 87 citations
17. Interpretese vs. Translationese: The Uniqueness of Human Strategies in Simultaneous Interpretation ↗Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · no summary yetHe He, Jordan Boyd-Graber, Hal Daumé III2016 · 86 citations
18. Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD ExamplesConference on Neural Information Processing Systems (NeurIPS)Abulhair Saparov, Richard Yuanzhe Pang, Vishakh Padmakumar, et al.2023 · 84 citations
19. Does Writing with Language Models Reduce Content Diversity? ↗arXiv:2309.05196 · no summary yetVishakh Padmakumar, He He2023 · 82 citations
20. Reasoning Models Know When They're Right: Probing Hidden States for Self-Verification ↗arXiv:2504.05419 · no summary yetAnqi Zhang et al.2025 · 52 citations
21. Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations ↗Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetChenglei Si et al.2023 · 42 citations
22. Syntax-based Rewriting for Simultaneous Machine Translation ↗Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · no summary yetHe He et al.2015 · 42 citations
23. Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations ↗arXiv (Cornell University) · no summary yetYanda Chen et al.2023 · 41 citations
24. ARGUS: Visualization of AI-Assisted Task Guidance in AR ↗IEEE Transactions on Visualization and Computer Graphics · no summary yetSonia Castelo et al.2023 · 38 citations
25. Language Models Learn to Mislead Humans via RLHF ↗arXiv (Cornell University) · no summary yetJiaxin Wen et al.2024 · 33 citations
26. Leveraging Implicit Feedback from Deployment Data in Dialogue ↗Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers) · no summary yetRichard Yuanzhe Pang et al.2024 · 12 citations
27. Amortized Noisy Channel Neural Machine Translation ↗Proceedings of the 15th International Conference on Natural Language Generation · no summary yetRichard Yuanzhe Pang, He He, Kyunghyun Cho2022 · 9 citations
28. Unsupervised Elicitation of Language Models ↗arXiv (Cornell University) · no summary yetJiaxin Wen et al.2025 · 2 citations
29. Show Your Work with Confidence: Confidence Bands for Tuning Curves ↗Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · no summary yetNicholas Lourie, Kyunghyun Cho, He He2024 · 2 citations
30. Hyperparameter Loss Surfaces Are Simple Near their Optima ↗arXiv (Cornell University) · no summary yetNicholas Lourie, He He, Kyunghyun Cho2025 · 1 citation
31. Spontaneous Reward Hacking in Iterative Self-Refinement ↗arXiv (Cornell University) · no summary yetJane Pan et al.2024 · 1 citation
32. PaintBench: Deterministic Evaluation of Precise Visual Editing ↗arXiv (Cornell University) · no summary yetXu, Kai et al.2026
33. Training Continuous Chain of Thought Models: A Tale of Two Regimes ↗arXiv (Cornell University) · no summary yetYerram Varun, He He, Eunsol Choi2026

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