The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task. - Amr Sharaf; Shi Feng 005; Khanh Nguyen; Kianté Brantley; Hal Daumé III (WMT, 2017)

Pathologies of Neural Models Make Interpretation Difficult. - Shi Feng 005; Eric Wallace; Alvin Grissom II; Mohit Iyyer; Pedro Rodriguez 001; Jordan L. Boyd-Graber (EMNLP, 2018)

Interpreting Neural Networks with Nearest Neighbors. - Eric Wallace; Shi Feng 005; Jordan L. Boyd-Graber (BlackboxNLP@EMNLP, 2018)

Right Answer for the Wrong Reason: Discovery and Mitigation. - Shi Feng 005; Eric Wallace; Mohit Iyyer; Pedro Rodriguez 001; Alvin Grissom II; Jordan L. Boyd-Graber (CoRR, 2018)

Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions. - Eric Wallace; Pedro Rodriguez 001; Shi Feng 005; Jordan L. Boyd-Graber (CoRR, 2018)

Misleading Failures of Partial-input Baselines. - Shi Feng 005; Eric Wallace; Jordan L. Boyd-Graber (ACL (1), 2019)

Universal Adversarial Triggers for Attacking and Analyzing NLP. - Eric Wallace; Shi Feng 005; Nikhil Kandpal; Matt Gardner 001; Sameer Singh 001 (EMNLP/IJCNLP (1), 2019)

Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation. - Sahil Singla 002; Eric Wallace; Shi Feng 005; Soheil Feizi (ICML, 2019)

What can AI do for me?: evaluating machine learning interpretations in cooperative play. - Shi Feng 005; Jordan L. Boyd-Graber (IUI, 2019)

Quizbowl: The Case for Incremental Question Answering. - Pedro Rodriguez 001; Shi Feng 005; Mohit Iyyer; He He 001; Jordan L. Boyd-Graber (CoRR, 2019)

Universal Adversarial Triggers for NLP. - Eric Wallace; Shi Feng 005; Nikhil Kandpal; Matt Gardner 001; Sameer Singh 001 (CoRR, 2019)

Trick Me If You Can: Human-in-the-loop Generation of Adversarial Question Answering Examples. - Eric Wallace; Pedro Rodriguez 001; Shi Feng 005; Ikuya Yamada; Jordan L. Boyd-Graber (Trans. Assoc. Comput. Linguistics, 2019)

Customizing Triggers with Concealed Data Poisoning. - Eric Wallace; Tony Z. Zhao; Shi Feng 005; Sameer Singh 001 (CoRR, 2020)

Calibrate Before Use: Improving Few-shot Performance of Language Models. - Zihao Zhao; Eric Wallace; Shi Feng 005; Dan Klein 001; Sameer Singh 001 (ICML, 2021)

Concealed Data Poisoning Attacks on NLP Models. - Eric Wallace; Tony Z. Zhao; Shi Feng 005; Sameer Singh 001 (NAACL-HLT, 2021)

Calibrate Before Use: Improving Few-Shot Performance of Language Models. - Tony Z. Zhao; Eric Wallace; Shi Feng 005; Dan Klein 001; Sameer Singh 001 (CoRR, 2021)

Learning to Explain Selectively: A Case Study on Question Answering. - Shi Feng 005; Jordan L. Boyd-Graber (EMNLP, 2022)

Active Example Selection for In-Context Learning. - Yiming Zhang 022; Shi Feng 005; Chenhao Tan (EMNLP, 2022)

Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations. - Chenglei Si; Dan Friedman; Nitish Joshi; Shi Feng 005; Danqi Chen 001; He He 001 (ACL (1), 2023)

Learning Human-Compatible Representations for Case-Based Decision Support. - Han Liu; Yizhou Tian; Chacha Chen; Shi Feng 005; Yuxin Chen 001; Chenhao Tan (ICLR, 2023)

Machine Explanations and Human Understanding. - Chacha Chen; Shi Feng 005; Amit Sharma 007; Chenhao Tan (Trans. Mach. Learn. Res., 2023)

A SMART Mnemonic Sounds like "Glue Tonic": Mixing LLMs with Student Feedback to Make Mnemonic Learning Stick. - Nishant Balepur; Matthew Shu; Alexander Miserlis Hoyle; Alison Robey; Shi Feng 005; Seraphina Goldfarb-Tarrant; Jordan L. Boyd-Graber (EMNLP, 2024)

KARL: Knowledge-Aware Retrieval and Representations aid Retention and Learning in Students. - Matthew Shu; Nishant Balepur; Shi Feng 005; Jordan L. Boyd-Graber (EMNLP, 2024)

Large Language Models Help Humans Verify Truthfulness - Except When They Are Convincingly Wrong. - Chenglei Si; Navita Goyal; Tongshuang Wu; Chen Zhao 009; Shi Feng 005; Hal Daumé III; Jordan L. Boyd-Graber (NAACL-HLT, 2024)

LLM Evaluators Recognize and Favor Their Own Generations. - Arjun Panickssery; Samuel R. Bowman; Shi Feng 005 (NeurIPS, 2024)

Spontaneous Reward Hacking in Iterative Self-Refinement. - Jane Pan; He He 001; Samuel R. Bowman; Shi Feng 005 (CoRR, 2024)

Whose Boat Does it Float? Improving Personalization in Preference Tuning via Inferred User Personas. - Nishant Balepur; Vishakh Padmakumar; Fumeng Yang; Shi Feng 005; Rachel Rudinger; Jordan Lee Boyd-Graber (ACL (1), 2025)

Adaptive Deployment of Untrusted LLMs Reduces Distributed Threats. - Jiaxin Wen; Vivek Hebbar; Caleb Larson; Aryan Bhatt; Ansh Radhakrishnan; Mrinank Sharma; Henry Sleight; Shi Feng 005; He He 001; Ethan Perez; Buck Shlegeris; Akbir Khan (ICLR, 2025)

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 001; Shi Feng 005 (ICLR, 2025)

Reverse Question Answering: Can an LLM Write a Question so Hard (or Bad) that it Can't Answer? - Nishant Balepur; Feng Gu; Abhilasha Ravichander; Shi Feng 005; Jordan Lee Boyd-Graber; Rachel Rudinger (NAACL (Short Papers), 2025)

Unsupervised Elicitation of Language Models. - Jiaxin Wen; Zachary Ankner; Arushi Somani; Peter Hase; Samuel Marks; Jacob Goldman-Wetzler; Linda Petrini; Henry Sleight; Collin Burns; He He 001; Shi Feng 005; Ethan Perez; et al. (CoRR, 2025)