Shi Feng

According to our database, Shi Feng authored at least 31 papers between 2017 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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, Shi Feng, Ethan Perez, Jan Leike

CoRR, June, 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, Jordan Lee Boyd-Graber, Rachel Rudinger

Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies, 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, Shi Feng

Proceedings of the Thirteenth International Conference on Learning Representations, 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, He He, Ethan Perez, Buck Shlegeris, Akbir Khan

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Whose Boat Does it Float? Improving Personalization in Preference Tuning via Inferred User Personas.

Nishant Balepur, Vishakh Padmakumar, Fumeng Yang, Shi Feng, Rachel Rudinger, Jordan Lee Boyd-Graber

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025

2024

Spontaneous Reward Hacking in Iterative Self-Refinement.

Jane Pan, He He, Samuel R. Bowman, Shi Feng

CoRR, 2024

LLM Evaluators Recognize and Favor Their Own Generations.

Arjun Panickssery, Samuel R. Bowman, Shi Feng

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Large Language Models Help Humans Verify Truthfulness - Except When They Are Convincingly Wrong.

Chenglei Si, Navita Goyal, Tongshuang Wu, Chen Zhao, Shi Feng, Hal Daumé III, Jordan L. Boyd-Graber

Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2024

KARL: Knowledge-Aware Retrieval and Representations aid Retention and Learning in Students.

Matthew Shu, Nishant Balepur, Shi Feng, Jordan L. Boyd-Graber

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

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, Seraphina Goldfarb-Tarrant, Jordan L. Boyd-Graber

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

2023

Machine Explanations and Human Understanding.

Chacha Chen, Shi Feng, Amit Sharma, Chenhao Tan

Trans. Mach. Learn. Res., 2023

Learning Human-Compatible Representations for Case-Based Decision Support.

Han Liu, Yizhou Tian, Chacha Chen, Shi Feng, Yuxin Chen, Chenhao Tan

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations.

Chenglei Si, Dan Friedman, Nitish Joshi, Shi Feng, Danqi Chen, He He

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

2022

Active Example Selection for In-Context Learning.

Yiming Zhang, Shi Feng, Chenhao Tan

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

Learning to Explain Selectively: A Case Study on Question Answering.

Shi Feng, Jordan L. Boyd-Graber

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

2021

Calibrate Before Use: Improving Few-Shot Performance of Language Models.

Tony Z. Zhao, Eric Wallace, Shi Feng, Dan Klein, Sameer Singh

CoRR, 2021

Concealed Data Poisoning Attacks on NLP Models.

Eric Wallace, Tony Z. Zhao, Shi Feng, Sameer Singh

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

Calibrate Before Use: Improving Few-shot Performance of Language Models.

Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, Sameer Singh

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Customizing Triggers with Concealed Data Poisoning.

Eric Wallace, Tony Z. Zhao, Shi Feng, Sameer Singh

CoRR, 2020

2019

Trick Me If You Can: Human-in-the-loop Generation of Adversarial Question Answering Examples.

Eric Wallace, Pedro Rodriguez, Shi Feng, Ikuya Yamada, Jordan L. Boyd-Graber

Trans. Assoc. Comput. Linguistics, 2019

Universal Adversarial Triggers for NLP.

Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, Sameer Singh

CoRR, 2019

Quizbowl: The Case for Incremental Question Answering.

Pedro Rodriguez, Shi Feng, Mohit Iyyer, He He, Jordan L. Boyd-Graber

CoRR, 2019

What can AI do for me?: evaluating machine learning interpretations in cooperative play.

Shi Feng, Jordan L. Boyd-Graber

Proceedings of the 24th International Conference on Intelligent User Interfaces, 2019

Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation.

Sahil Singla, Eric Wallace, Shi Feng, Soheil Feizi

Proceedings of the 36th International Conference on Machine Learning, 2019

Universal Adversarial Triggers for Attacking and Analyzing NLP.

Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, Sameer Singh

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019

Misleading Failures of Partial-input Baselines.

Shi Feng, Eric Wallace, Jordan L. Boyd-Graber

Proceedings of the 57th Conference of the Association for Computational Linguistics, 2019

2018

Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions.

Eric Wallace, Pedro Rodriguez, Shi Feng, Jordan L. Boyd-Graber

CoRR, 2018

Right Answer for the Wrong Reason: Discovery and Mitigation.

Shi Feng, Eric Wallace, Mohit Iyyer, Pedro Rodriguez, Alvin Grissom II, Jordan L. Boyd-Graber

CoRR, 2018

Interpreting Neural Networks with Nearest Neighbors.

Eric Wallace, Shi Feng, Jordan L. Boyd-Graber

Proceedings of the Workshop: Analyzing and Interpreting Neural Networks for NLP, 2018

Pathologies of Neural Models Make Interpretation Difficult.

Shi Feng, Eric Wallace, Alvin Grissom II, Mohit Iyyer, Pedro Rodriguez, Jordan L. Boyd-Graber

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31, 2018

2017

The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task.

Amr Sharaf, Shi Feng, Khanh Nguyen, Kianté Brantley, Hal Daumé III

Proceedings of the Second Conference on Machine Translation, 2017