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ORCIDShi Feng 0005
Person information
- affiliation: New York University, NY, USA
- affiliation: University of Chicago, IL, USA
- affiliation: University of Maryland, MD, USA
- affiliation: Shanghai Jiaotong University, China
Other persons with the same name
- Shi Feng — disambiguation page
- Shi Feng 0001
![[0000-0002-2846-7652]](https://devdomain4test.ir/img/orcid-mark.12x12.png "0000-0002-2846-7652")
— Northeastern University, School of Computer Science and Engineering, Shenyang, China - Shi Feng 0002 — Harvard University, John A. Paulson School of Engineering and Applied Sciences, Cambridge, MA, USA (and 1 more)
- Shi Feng 0003 — Beijing Institute of Technology, Department of Computer Science and Engineering, Beijing, China
- Shi Feng 0004 — Purdue University, West Lafayette, IN, USA (and 1 more)
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2020 – today
- 2026
[i27]Yuyang Jiang, Chacha Chen, Teng Wu, Liwen Sun, Han Liu, Shi Feng, Chenhao Tan:
Collaborative Disagreement Resolution for Scalable Oversight. CoRR abs/2607.01251 (2026)- 2025
- [c24]Nishant Balepur, Vishakh Padmakumar, Fumeng Yang, Shi Feng, Rachel Rudinger, Jordan Lee Boyd-Graber:
Whose Boat Does it Float? Improving Personalization in Preference Tuning via Inferred User Personas. ACL (1) 2025: 3371-3393 - [c23]Nishant Balepur, Matthew Shu, Yoo Yeon Sung, Seraphina Goldfarb-Tarrant, Shi Feng, Fumeng Yang, Rachel Rudinger, Jordan Lee Boyd-Graber:
A Good Plan is Hard to Find: Aligning Models with Preferences is Misaligned with What Helps Users. EMNLP 2025: 11568-11595 - [c22]Jiaxin Wen, Vivek Hebbar, Caleb Larson, Aryan Bhatt, Ansh Radhakrishnan, Mrinank Sharma, Henry Sleight, Shi Feng, He He, Ethan Perez, Buck Shlegeris, Akbir Khan:
Adaptive Deployment of Untrusted LLMs Reduces Distributed Threats. ICLR 2025 - [c21]Jiaxin Wen, Ruiqi Zhong, Akbir Khan, Ethan Perez, Jacob Steinhardt, Minlie Huang, Samuel R. Bowman, He He, Shi Feng:
Language Models Learn to Mislead Humans via RLHF. ICLR 2025 - [c20]Nishant Balepur, Feng Gu, Abhilasha Ravichander, Shi Feng, Jordan Lee Boyd-Graber, Rachel Rudinger:
Reverse Question Answering: Can an LLM Write a Question so Hard (or Bad) that it Can't Answer? NAACL (Short Papers) 2025: 44-64 - [c19]Jiaxin Wen, Chenglei Si, Yueh-Han Chen, He He, Shi Feng:
Predicting Empirical AI Research Outcomes with Language Models. NeurIPS 2025 - [i26]Nishant Balepur, Vishakh Padmakumar, Fumeng Yang, Shi Feng, Rachel Rudinger, Jordan Lee Boyd-Graber:
Whose Boat Does it Float? Improving Personalization in Preference Tuning via Inferred User Personas. CoRR abs/2501.11549 (2025) - [i25]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:
Unsupervised Elicitation of Language Models. CoRR abs/2506.10139 (2025) - [i24]Nishant Balepur, Matthew Shu, Yoo Yeon Sung, Seraphina Goldfarb-Tarrant, Shi Feng, Fumeng Yang, Rachel Rudinger, Jordan Lee Boyd-Graber:
A Good Plan is Hard to Find: Aligning Models with Preferences is Misaligned with What Helps Users. CoRR abs/2509.18632 (2025) - 2024
- [c18]Matthew Shu, Nishant Balepur, Shi Feng, Jordan L. Boyd-Graber:
KARL: Knowledge-Aware Retrieval and Representations aid Retention and Learning in Students. EMNLP 2024: 14161-14178 - [c17]Nishant Balepur, Matthew Shu, Alexander Miserlis Hoyle, Alison Robey, Shi Feng, Seraphina Goldfarb-Tarrant, Jordan L. Boyd-Graber:
A SMART Mnemonic Sounds like "Glue Tonic": Mixing LLMs with Student Feedback to Make Mnemonic Learning Stick. EMNLP 2024: 14202-14225 - [c16]Chenglei Si, Navita Goyal, Tongshuang Wu, Chen Zhao, Shi Feng, Hal Daumé III, Jordan L. Boyd-Graber:
Large Language Models Help Humans Verify Truthfulness - Except When They Are Convincingly Wrong. NAACL-HLT 2024: 1459-1474 - [c15]Arjun Panickssery, Samuel R. Bowman, Shi Feng:
LLM Evaluators Recognize and Favor Their Own Generations. NeurIPS 2024 - [i23]Matthew Shu, Nishant Balepur, Shi Feng, Jordan L. Boyd-Graber:
KARL: Knowledge-Aware Retrieval and Representations aid Retention and Learning in Students. CoRR abs/2402.12291 (2024) - [i22]Arjun Panickssery, Samuel R. Bowman, Shi Feng:
LLM Evaluators Recognize and Favor Their Own Generations. CoRR abs/2404.13076 (2024) - [i21]Nishant Balepur, Matthew Shu, Alexander Miserlis Hoyle, Alison Robey, Shi Feng, Seraphina Goldfarb-Tarrant, Jordan L. Boyd-Graber:
A SMART Mnemonic Sounds like "Glue Tonic": Mixing LLMs with Student Feedback to Make Mnemonic Learning Stick. CoRR abs/2406.15352 (2024) - [i20]Jane Pan, He He, Samuel R. Bowman, Shi Feng:
Spontaneous Reward Hacking in Iterative Self-Refinement. CoRR abs/2407.04549 (2024) - [i19]Jiaxin Wen, Ruiqi Zhong, Akbir Khan, Ethan Perez, Jacob Steinhardt, Minlie Huang, Samuel R. Bowman, He He, Shi Feng:
Language Models Learn to Mislead Humans via RLHF. CoRR abs/2409.12822 (2024) - [i18]Nishant Balepur, Feng Gu, Abhilasha Ravichander, Shi Feng, Jordan L. Boyd-Graber, Rachel Rudinger:
Reverse Question Answering: Can an LLM Write a Question so Hard (or Bad) that it Can't Answer? CoRR abs/2410.15512 (2024) - [i17]Jiaxin Wen, Vivek Hebbar, Caleb Larson, Aryan Bhatt, Ansh Radhakrishnan, Mrinank Sharma, Henry Sleight, Shi Feng, He He, Ethan Perez, Buck Shlegeris, Akbir Khan:
Adaptive Deployment of Untrusted LLMs Reduces Distributed Threats. CoRR abs/2411.17693 (2024) - 2023
- [j2]Chacha Chen, Shi Feng, Amit Sharma, Chenhao Tan:
Machine Explanations and Human Understanding. Trans. Mach. Learn. Res. 2023 (2023) - [c14]Chenglei Si, Dan Friedman, Nitish Joshi, Shi Feng, Danqi Chen, He He:
Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations. ACL (1) 2023: 11289-11310 - [c13]Chacha Chen, Shi Feng, Amit Sharma, Chenhao Tan:
Machine Explanations and Human Understanding. FAccT 2023: 1 - [c12]Han Liu, Yizhou Tian, Chacha Chen, Shi Feng, Yuxin Chen, Chenhao Tan:
Learning Human-Compatible Representations for Case-Based Decision Support. ICLR 2023 - [i16]Han Liu, Yizhou Tian, Chacha Chen, Shi Feng, Yuxin Chen, Chenhao Tan:
Learning Human-Compatible Representations for Case-Based Decision Support. CoRR abs/2303.04809 (2023) - [i15]Chenglei Si, Dan Friedman, Nitish Joshi, Shi Feng, Danqi Chen, He He:
Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations. CoRR abs/2305.13299 (2023) - [i14]Chenglei Si, Navita Goyal, Sherry Tongshuang Wu, Chen Zhao, Shi Feng, Hal Daumé III, Jordan L. Boyd-Graber:
Large Language Models Help Humans Verify Truthfulness - Except When They Are Convincingly Wrong. CoRR abs/2310.12558 (2023) - 2022
- [c11]Shi Feng, Jordan L. Boyd-Graber:
Learning to Explain Selectively: A Case Study on Question Answering. EMNLP 2022: 8372-8382 - [c10]Yiming Zhang, Shi Feng, Chenhao Tan:
Active Example Selection for In-Context Learning. EMNLP 2022: 9134-9148 - [i13]Chacha Chen, Shi Feng, Amit Sharma, Chenhao Tan:
Machine Explanations and Human Understanding. CoRR abs/2202.04092 (2022) - [i12]Yiming Zhang, Shi Feng, Chenhao Tan:
Active Example Selection for In-Context Learning. CoRR abs/2211.04486 (2022) - 2021
- [c9]Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, Sameer Singh:
Calibrate Before Use: Improving Few-shot Performance of Language Models. ICML 2021: 12697-12706 - [c8]Eric Wallace, Tony Z. Zhao, Shi Feng, Sameer Singh:
Concealed Data Poisoning Attacks on NLP Models. NAACL-HLT 2021: 139-150 - [i11]Tony Z. Zhao, Eric Wallace, Shi Feng, Dan Klein, Sameer Singh:
Calibrate Before Use: Improving Few-Shot Performance of Language Models. CoRR abs/2102.09690 (2021) - 2020
- [i10]Eric Wallace, Tony Z. Zhao, Shi Feng, Sameer Singh:
Customizing Triggers with Concealed Data Poisoning. CoRR abs/2010.12563 (2020)
2010 – 2019
- 2019
- [j1]Eric Wallace, Pedro Rodriguez, Shi Feng, Ikuya Yamada, Jordan L. Boyd-Graber:
Trick Me If You Can: Human-in-the-loop Generation of Adversarial Question Answering Examples. Trans. Assoc. Comput. Linguistics 7: 387-401 (2019) - [c7]Shi Feng, Eric Wallace, Jordan L. Boyd-Graber:
Misleading Failures of Partial-input Baselines. ACL (1) 2019: 5533-5538 - [c6]Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, Sameer Singh:
Universal Adversarial Triggers for Attacking and Analyzing NLP. EMNLP/IJCNLP (1) 2019: 2153-2162 - [c5]Sahil Singla, Eric Wallace, Shi Feng, Soheil Feizi:
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation. ICML 2019: 5848-5856 - [c4]Shi Feng, Jordan L. Boyd-Graber:
What can AI do for me?: evaluating machine learning interpretations in cooperative play. IUI 2019: 229-239 - [i9]Sahil Singla, Eric Wallace, Shi Feng, Soheil Feizi:
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation. CoRR abs/1902.00407 (2019) - [i8]Pedro Rodriguez, Shi Feng, Mohit Iyyer, He He, Jordan L. Boyd-Graber:
Quizbowl: The Case for Incremental Question Answering. CoRR abs/1904.04792 (2019) - [i7]Shi Feng, Eric Wallace, Jordan L. Boyd-Graber:
Misleading Failures of Partial-input Baselines. CoRR abs/1905.05778 (2019) - [i6]Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, Sameer Singh:
Universal Adversarial Triggers for NLP. CoRR abs/1908.07125 (2019) - 2018
- [c3]Eric Wallace, Shi Feng, Jordan L. Boyd-Graber:
Interpreting Neural Networks with Nearest Neighbors. BlackboxNLP@EMNLP 2018: 136-144 - [c2]Shi Feng, Eric Wallace, Alvin Grissom II, Mohit Iyyer, Pedro Rodriguez, Jordan L. Boyd-Graber:
Pathologies of Neural Models Make Interpretation Difficult. EMNLP 2018: 3719-3728 - [i5]Shi Feng, Eric Wallace, Mohit Iyyer, Pedro Rodriguez, Alvin Grissom II, Jordan L. Boyd-Graber:
Right Answer for the Wrong Reason: Discovery and Mitigation. CoRR abs/1804.07781 (2018) - [i4]Eric Wallace, Pedro Rodriguez, Shi Feng, Jordan L. Boyd-Graber:
Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions. CoRR abs/1809.02701 (2018) - [i3]Eric Wallace, Shi Feng, Jordan L. Boyd-Graber:
Interpreting Neural Networks With Nearest Neighbors. CoRR abs/1809.02847 (2018) - [i2]Shi Feng, Jordan L. Boyd-Graber:
What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative Play. CoRR abs/1810.09648 (2018) - 2017
- [c1]Amr Sharaf, Shi Feng, Khanh Nguyen, Kianté Brantley, Hal Daumé III:
The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task. WMT 2017: 667-673 - [i1]Amr Sharaf, Shi Feng, Khanh Nguyen, Kianté Brantley, Hal Daumé III:
The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task. CoRR abs/1708.01318 (2017)
Coauthor Index
aka: Jordan Lee Boyd-Graber
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