| 2025 | ACL | When Benchmarks Talk: Re-Evaluating Code LLMs with Interactive Feedback. | Jane Pan, Ryan Shar, Jacob Pfau, Ameet Talwalkar, He He, Valerie Chen |
| 2025 | COLING | Towards Consistent Natural-Language Explanations via Explanation-Consistency Finetuning. | Yanda Chen, Chandan Singh, Xiaodong Liu, Simiao Zuo, Bin Yu, He He, Jianfeng Gao |
| 2025 | ICLR | Transformers Struggle to Learn to Search. | Abulhair Saparov, Srushti Ajay Pawar, Shreyas Pimpalgaonkar, Nitish Joshi, Richard Yuanzhe Pang, Vishakh Padmakumar, Mehran Kazemi, Najoung Kim, He He |
| 2025 | ICLR | Adaptive Deployment of Untrusted LLMs Reduces Distributed Threats. | Jiaxin Wen, Vivek Hebbar, Caleb Larson, Aryan Bhatt, Ansh Radhakrishnan, Mrinank Sharma, Henry Sleight, Shi Feng, He He, Ethan Perez, Buck Shlegeris, Akbir Khan |
| 2025 | ICLR | Language Models Learn to Mislead Humans via RLHF. | Jiaxin Wen, Ruiqi Zhong, Akbir Khan, Ethan Perez, Jacob Steinhardt, Minlie Huang, Samuel R. Bowman, He He, Shi Feng |
| 2024 | ACL | Parallel Structures in Pre-training Data Yield In-Context Learning. | Yanda Chen, Chen Zhao, Zhou Yu, Kathleen R. McKeown, He He |
| 2024 | ACL | Your Co-Workers Matter: Evaluating Collaborative Capabilities of Language Models in Blocks World. | Guande Wu, Chen Zhao, Cludio T. Silva, He He |
| 2024 | APCC | Optimization of Vehicular Network Resource Allocation Based on MAAC Algorithm. | Jun-Han Wang, He He, Kosuke Tamura, Shun Kojima, Jaesang Cha, Chang-Jun Ahn |
| 2024 | EACL | Leveraging Implicit Feedback from Deployment Data in Dialogue. | Richard Yuanzhe Pang, Stephen Roller, Kyunghyun Cho, He He, Jason Weston |
| 2024 | EMNLP | Personas as a Way to Model Truthfulness in Language Models. | Nitish Joshi, Javier Rando, Abulhair Saparov, Najoung Kim, He He |
| 2024 | EMNLP | LLMs Are Prone to Fallacies in Causal Inference. | Nitish Joshi, Abulhair Saparov, Yixin Wang, He He |
| 2024 | ICLR | Does Writing with Language Models Reduce Content Diversity? | Vishakh Padmakumar, He He |
| 2024 | ICML | Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations. | Yanda Chen, Ruiqi Zhong, Narutatsu Ri, Chen Zhao, He He, Jacob Steinhardt, Zhou Yu, Kathleen R. McKeown |
| 2024 | NAACL | Show Your Work with Confidence: Confidence Bands for Tuning Curves. | Nicholas Lourie, Kyunghyun Cho, He He |
| 2023 | ACL | Reward Gaming in Conditional Text Generation. | Richard Yuanzhe Pang, Vishakh Padmakumar, Thibault Sellam, Ankur P. Parikh, He He |
| 2023 | ACL | Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations. | Chenglei Si, Dan Friedman, Nitish Joshi, Shi Feng, Danqi Chen, He He |
| 2023 | ACL | Efficient Shapley Values Estimation by Amortization for Text Classification. | Chenghao Yang, Fan Yin, He He, Kai-Wei Chang, Xiaofei Ma, Bing Xiang |
| 2023 | EACL | Robustification of Multilingual Language Models to Real-world Noise in Crosslingual Zero-shot Settings with Robust Contrastive Pretraining. | Asa Cooper Stickland, Sailik Sengupta, Jason Krone, Saab Mansour, He He |
| 2023 | EACL | How do decoding algorithms distribute information in dialogue responses? | Saranya Venkatraman, He He, David Reitter |
| 2023 | EMNLP | Creative Natural Language Generation. | Tuhin Chakrabarty, Vishakh Padmakumar, He He, Nanyun Peng |
| 2023 | EMNLP | On the Relation between Sensitivity and Accuracy in In-Context Learning. | Yanda Chen, Chen Zhao, Zhou Yu, Kathleen R. McKeown, He He |
| 2023 | ICLR | Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought. | Abulhair Saparov, He He |
| 2023 | ICML | Extrapolative Controlled Sequence Generation via Iterative Refinement. | Vishakh Padmakumar, Richard Yuanzhe Pang, He He, Ankur P. Parikh |
| 2022 | ACL | Meta-learning via Language Model In-context Tuning. | Yanda Chen, Ruiqi Zhong, Sheng Zha, George Karypis, He He |
| 2022 | ACL | An Investigation of the (In)effectiveness of Counterfactually Augmented Data. | Nitish Joshi, He He |
| 2022 | ACL | Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization. | Faisal Ladhak, Esin Durmus, He He, Claire Cardie, Kathleen R. McKeown |
| 2022 | EMNLP | Help me write a Poem - Instruction Tuning as a Vehicle for Collaborative Poetry Writing. | Tuhin Chakrabarty, Vishakh Padmakumar, He He |
| 2022 | EMNLP | Are All Spurious Features in Natural Language Alike? An Analysis through a Causal Lens. | Nitish Joshi, Xiang Pan, He He |
| 2022 | EMNLP | Improving Faithfulness by Augmenting Negative Summaries from Fake Documents. | Tianshu Wang, Faisal Ladhak, Esin Durmus, He He |
| 2022 | NAACL | Machine-in-the-Loop Rewriting for Creative Image Captioning. | Vishakh Padmakumar, He He |
| 2022 | NAACL | Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning. | Vishakh Padmakumar, Leonard Lausen, Miguel Ballesteros, Sheng Zha, He He, George Karypis |
| 2022 | NAACL | QuALITY: Question Answering with Long Input Texts, Yes! | Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, Nikita Nangia, Jason Phang, Angelica Chen, Vishakh Padmakumar, Johnny Ma, Jana Thompson, He He, Samuel R. Bowman |
| 2021 | EACL | Unsupervised Extractive Summarization using Pointwise Mutual Information. | Vishakh Padmakumar, He He |
| 2021 | EMNLP | Types of Out-of-Distribution Texts and How to Detect Them. | Udit Arora, William Huang, He He |
| 2021 | EMNLP | Robustness and Adversarial Examples in Natural Language Processing. | Kai-Wei Chang, He He, Robin Jia, Sameer Singh |
| 2021 | ICLR | Text Generation by Learning from Demonstrations. | Richard Yuanzhe Pang, He He |
| 2021 | VTC | Variable Frame Splitting for Polar Coded MIMO E-SDM in Fast Fading Channel. | He He, Shun Kojima, Kentaro Yonei, Kazuki Maruta, Chang-Jun Ahn |
| 2020 | ACL | FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization. | Esin Durmus, He He, Mona T. Diab |
| 2020 | IJCAI | Partial Adversarial Behavior Deception in Security Games. | Thanh Hong Nguyen, Arunesh Sinha, He He |
| 2019 | EMNLP | Dive into Deep Learning for Natural Language Processing. | Haibin Lin, Xingjian Shi, Leonard Lausen, Aston Zhang, He He, Sheng Zha, Alexander J. Smola |
| 2019 | NAACL | Pun Generation with Surprise. | He He, Nanyun Peng, Percy Liang |
| 2019 | SMC | Channel and Trials Selection for Reducing Covariate Shift in EEG-based Brain-Computer Interfaces. | He He, Dongrui Wu |
| 2019 | SIGdial | A Dynamic Strategy Coach for Effective Negotiation. | Yiheng Zhou, He He, Alan W. Black, Yulia Tsvetkov |
| 2018 | ACL | Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context. | Urvashi Khandelwal, He He, Peng Qi, Dan Jurafsky |
| 2018 | EMNLP | QuAC: Question Answering in Context. | Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, Luke Zettlemoyer |
| 2018 | EMNLP | Decoupling Strategy and Generation in Negotiation Dialogues. | He He, Derek Chen, Anusha Balakrishnan, Percy Liang |
| 2018 | NAACL | Delete, Retrieve, Generate: a Simple Approach to Sentiment and Style Transfer. | Juncen Li, Robin Jia, He He, Percy Liang |
| 2017 | ACL | Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings. | He He, Anusha Balakrishnan, Mihail Eric, Percy Liang |
| 2017 | ICONIP | Transfer Learning Enhanced Common Spatial Pattern Filtering for Brain Computer Interfaces (BCIs): Overview and a New Approach. | He He, Dongrui Wu |
| 2016 | ICML | Opponent Modeling in Deep Reinforcement Learning. | He He, Jordan L. Boyd-Graber |
| 2016 | NAACL | Interpretese vs. Translationese: The Uniqueness of Human Strategies in Simultaneous Interpretation. | He He, Jordan L. Boyd-Graber, Hal Daum III |
| 2016 | WACV | Object detection in 20 questions. | Xi Stephen Chen, He He, Larry S. Davis |
| 2015 | EMNLP | Syntax-based Rewriting for Simultaneous Machine Translation. | He He, Alvin Grissom II, John Morgan, Jordan L. Boyd-Graber, Hal Daum III |
| 2015 | FUSION | Crowdsourcing with multi-dimensional trust. | Xiangyang Liu, He He, John S. Baras |
| 2015 | NAACL | Hands-on Learning to Search for Structured Prediction. | Hal Daum III, John Langford, Kai-Wei Chang, He He, Sudha Rao |
| 2014 | EMNLP | Don't Until the Final Verb Wait: Reinforcement Learning for Simultaneous Machine Translation. | Alvin Grissom II, He He, Jordan L. Boyd-Graber, John Morgan, Hal Daum III |
| 2013 | EMNLP | Dynamic Feature Selection for Dependency Parsing. | He He, Hal Daum III, Jason Eisner |
| 2012 | EMNLP | Besting the Quiz Master: Crowdsourcing Incremental Classification Games. | Jordan L. Boyd-Graber, Brianna Satinoff, He He, Hal Daum III |
| 2011 | CVPR | Single image super-resolution using Gaussian process regression. | He He, Wan-Chi Siu |
| 2010 | ICPR | Rare Class Classification by Support Vector Machine. | He He, Ali Ghodsi |