He, He

QuAC: Question Answering in Context

Choi, E., He, H., Iyyer, M., Yatskar, M., Yih, W.-T., Choi, Y., Liang, P., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Topic ModelingSpeech and dialogue systems

Cited by 714

Solving olympiad geometry without human demonstrations

Trinh, T. H., Wu, Y., Le, Q. V., He, H., & Luong, T. (2024) · Nature

AI-based Problem Solving and PlanningArtificial Intelligence Applications

Cited by 311

FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization

Durmus, E., He, H., & Diab, M. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Topic ModelingAdvanced Text Analysis Techniques

Cited by 306

Sharp nearby, fuzzy far away: How neural language models use context

Khandelwal, U., He, H., Qi, P., & Jurafsky, D. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 303

Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context

Khandelwal, U., He, H., Qi, P., & Jurafsky, D. (2018) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 302

Decoupling Strategy and Generation in Negotiation Dialogues

He, H., Chen, D., Balakrishnan, A., & Liang, P. (2018) · arXiv

Multi-Agent Systems and NegotiationSpeech and dialogue systems

Cited by 130

QuALITY: Question Answering with Long Input Texts, Yes!

Pang, R. Y., Parrish, A., Joshi, N., Nangia, N., Phang, J., Chen, A., Padmakumar, V., Ma, J., Thompson, J., He, H., et al. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingNatural Language Processing Techniques

Cited by 51

Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization

Ladhak, F., Durmus, E., He, H., Cardie, C., & McKeown, K. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingAdvanced Text Analysis Techniques

Cited by 46

Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought

Saparov, A., & He, H. (2022) · arXiv

Topic ModelingLogic, Reasoning, and Knowledge

Cited by 46

Does Writing with Language Models Reduce Content Diversity?

Padmakumar, V., & He, H. (2023) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 21

Foundational Challenges in Assuring Alignment and Safety of Large Language Models

Anwar, U., Saparov, A., Rando, J., Paleka, D., Turpin, M., Hase, P., Lubana, E. S., Jenner, E., Casper, S., Sourbut, O., et al. (2024) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 19

Iterative Reasoning Preference Optimization

Pang, R. Y., Yuan, W., Cho, K., He, H., Sukhbaatar, S., & Weston, J. (2024) · arXiv

Constraint Satisfaction and OptimizationLogic, Reasoning, and Knowledge

Cited by 15

QuALITY: Question Answering with Long Input Texts, Yes!

Pang, R. Y., Parrish, A., Joshi, N., Nangia, N., Phang, J., Chen, A., Padmakumar, V., Ma, J., Thompson, J., He, H., et al. (2021) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 10

Machine-in-the-Loop Rewriting for Creative Image Captioning

Padmakumar, V., & He, H. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Natural Language Processing TechniquesSpeech and dialogue systems

Cited by 7

Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations

Chen, Y., Zhong, R., Ri, N., Zhao, C., He, H., Steinhardt, J., Yu, Z., & McKeown, K. (2023) · arXiv

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 5

Language Models Learn to Mislead Humans via RLHF

Wen, J., Zhong, R., Khan, A., Perez, E., Steinhardt, J., Huang, M., Bowman, S. R., He, H., & Feng, S. (2024) · arXiv

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 4

Reward Gaming in Conditional Text Generation

Pang, R. Y., Padmakumar, V., Sellam, T., Parikh, A. P., & He, H. (2022) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 2

Adaptive Deployment of Untrusted LLMs Reduces Distributed Threats

Wen, J., Hebbar, V., Larson, C., Bhatt, A., Radhakrishnan, A., Sharma, M., Sleight, H., Feng, S., He, H., Perez, E., et al. (2024) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 1

Pragmatic Radiology Report Generation

Nguyen, D., Chen, C., He, H., & Tan, C. (2023) · arXiv

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 1

Leveraging Implicit Feedback from Deployment Data in Dialogue

Pang, R. Y., Roller, S., Cho, K., He, H., & Weston, J. (2023) · arXiv

Speech and dialogue systemsAI in Service Interactions

Cited by 1

Unsupervised Elicitation of Language Models

Wen, J., Ankner, Z., Somani, A., Hase, P., Marks, S., Goldman-Wetzler, J., Petrini, L., Sleight, H., Burns, C., He, H., et al. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0

When Benchmarks Talk: Re-Evaluating Code LLMs with Interactive Feedback

Pan, J., Shar, R., Pfau, J., Talwalkar, A., He, H., & Chen, V. (2025) · arXiv

Software Engineering ResearchSoftware Testing and Debugging Techniques

Cited by 0