Peng, Nanyun

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

The Woman Worked as a Babysitter: On Biases in Language Generation

Sheng, E., Chang, K.-W., Natarajan, P., & Peng, N. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Ethics and Social Impacts of AINatural Language Processing Techniques

Cited by 537

"Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters

Wan, Y., Pu, G., Sun, J., Garimella, A., Chang, K.-W., & Peng, N. (2023) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 175

Societal Biases in Language Generation: Progress and Challenges

Sheng, E., Chang, K.-W., Natarajan, P., & Peng, N. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Ethics and Social Impacts of AINatural Language Processing Techniques

Cited by 131

Re3: Generating Longer Stories With Recursive Reprompting and Revision

Yang, K., Tian, Y., Peng, N., & Klein, D. (2022) · arXiv

Artificial Intelligence in GamesTopic Modeling

Cited by 91

On Prompt-Driven Safeguarding for Large Language Models

Zheng, C., Yin, F., Zhou, H., Meng, F., Zhou, J., Chang, K.-W., Huang, M., & Peng, N. (2024) · arXiv

Adversarial Robustness in Machine LearningHate Speech and Cyberbullying Detection

Cited by 46

Evaluating Large Language Models on Controlled Generation Tasks

Sun, J., Tian, Y., Zhou, W., Xu, N., Hu, Q., Gupta, R., Wieting, J., Peng, N., & Ma, X. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 29

3DLLM-Mem: Long-Term Spatial-Temporal Memory for Embodied 3D Large Language Model

Hu, W., Hong, Y., Wang, Y., Gao, L., Wei, Z., Yao, X., Peng, N., Bitton, Y., Szpektor, I., & Chang, K.-W. (2025) · Advances in Neural Information Processing Systems 38

Robot Manipulation and LearningMultimodal Machine Learning Applications

Cited by 27

DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations

Ghazarian, S., Wen, N., Galstyan, A., & Peng, N. (2022) · arXiv

Speech and dialogue systemsNatural Language Processing Techniques

Cited by 26

FOAM: A Follower-aware Speaker Model For Vision-and-Language Navigation

Dou, Z.-Y., & Peng, N. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Multimodal Machine Learning ApplicationsReinforcement Learning in Robotics

Cited by 16

Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue

Gu, J.-C., Xu, H.-X., Ma, J.-Y., Lu, P., Ling, Z.-H., Chang, K.-W., & Peng, N. (2024) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 16

Next Steps for Human-Centered Generative AI: A Technical Perspective

Chen, X. '., Burke, J., Du, R., Hong, M. K., Jacobs, J., Laban, P., Li, D., Peng, N., Willis, K. D. D., Wu, C.-S., et al. (2023) · arXiv

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 13

A large-scale randomized study of large language model feedback in peer review

Thakkar, N., Yuksekgonul, M., Silberg, J., Garg, A., Peng, N., Sha, F., Yu, R., Vondrick, C., & Zou, J. (2026) · Nature Machine Intelligence

Publishing and Scholarly CommunicationAcademic Writing and Publishing

Cited by 11

Re-ReST: Reflection-Reinforced Self-Training for Language Agents

Dou, Z.-Y., Yang, C.-F., Wu, X., Chang, K.-W., & Peng, N. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 6

Are Large Language Models Capable of Generating Human-Level Narratives?

Tian, Y., Huang, T., Liu, M., Jiang, D., Spangher, A., Chen, M., May, J., & Peng, N. (2024) · arXiv

Topic ModelingArtificial Intelligence in Games

Cited by 5

Do LLMs Plan Like Human Writers? Comparing Journalist Coverage of Press Releases with LLMs

Spangher, A., Peng, N., Gehrmann, S., & Dredze, M. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Journalism and Media StudiesNews Media Framing and Political Communication

Cited by 5

Can LLM feedback enhance review quality? A randomized study of 20K reviews at ICLR 2025

Thakkar, N., Yuksekgonul, M., Silberg, J., Garg, A., Peng, N., Sha, F., Yu, R., Vondrick, C., & Zou, J. (2025) · arXiv

Publishing and Scholarly CommunicationAcademic Writing and Publishing

Cited by 4

LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints

Palmeira Ferraz, T., Mehta, K., Lin, Y.-H., Chang, H.-S., Oraby, S., Liu, S., Subramanian, V., Chung, T., Bansal, M., & Peng, N. (2024) · Findings of the Association for Computational Linguistics: EMNLP 2024

Topic ModelingNatural Language Processing Techniques

Cited by 3

Evaluating Large Language Models on Controlled Generation Tasks

Sun, J., Tian, Y., Zhou, W., Xu, N., Hu, Q., Gupta, R., Wieting, J. F., Peng, N., & Ma, X. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1