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Shi Feng
Shi Feng
Verified email at uchicago.edu - Homepage
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Cited by
Cited by
Year
Calibrate before use: Improving few-shot performance of language models
Z Zhao, E Wallace, S Feng, D Klein, S Singh
International conference on machine learning, 12697-12706, 2021
9372021
Universal adversarial triggers for attacking and analyzing NLP
E Wallace, S Feng, N Kandpal, M Gardner, S Singh
arXiv preprint arXiv:1908.07125, 2019
7682019
Pathologies of Neural Models Make Interpretations Difficult
S Feng, E Wallace, A Grissom II, M Iyyer, P Rodriguez, J Boyd-Graber
EMNLP, 2018
3472018
Trick me if you can: Human-in-the-loop generation of adversarial examples for question answering
E Wallace, P Rodriguez, S Feng, I Yamada, J Boyd-Graber
Transactions of the Association for Computational Linguistics 7, 387-401, 2019
1622019
What can ai do for me? evaluating machine learning interpretations in cooperative play
S Feng, J Boyd-Graber
Proceedings of the 24th International Conference on Intelligent User …, 2019
1472019
Concealed data poisoning attacks on NLP models
E Wallace, TZ Zhao, S Feng, S Singh
arXiv preprint arXiv:2010.12563, 2020
137*2020
Active example selection for in-context learning
Y Zhang, S Feng, C Tan
arXiv preprint arXiv:2211.04486, 2022
1022022
Knowledge-based semantic embedding for machine translation
C Shi, S Liu, S Ren, S Feng, M Li, M Zhou, X Sun, H Wang
Proceedings of the 54th Annual Meeting of the Association for Computational …, 2016
852016
Improving Attention Modeling with Implicit Distortion and Fertility for Machine Translation
S Feng, S Liu, N Yang, M Li, M Zhou, KQ Zhu
COLING, 2016
74*2016
Interpreting neural networks with nearest neighbors
E Wallace, S Feng, J Boyd-Graber
arXiv preprint arXiv:1809.02847, 2018
582018
Understanding impacts of high-order loss approximations and features in deep learning interpretation
S Singla, E Wallace, S Feng, S Feizi
International Conference on Machine Learning, 5848-5856, 2019
512019
Misleading failures of partial-input baselines
S Feng, E Wallace, J Boyd-Graber
arXiv preprint arXiv:1905.05778, 2019
402019
Quizbowl: The case for incremental question answering
P Rodriguez, S Feng, M Iyyer, H He, J Boyd-Graber
arXiv preprint arXiv:1904.04792, 2019
392019
Measuring inductive biases of in-context learning with underspecified demonstrations
C Si, D Friedman, N Joshi, S Feng, D Chen, H He
arXiv preprint arXiv:2305.13299, 2023
222023
Machine explanations and human understanding
C Chen, S Feng, A Sharma, C Tan
arXiv preprint arXiv:2202.04092, 2022
202022
Human-centered evaluation of explanations
J Boyd-Graber, S Carton, S Feng, QV Liao, T Lombrozo, A Smith-Renner, ...
Proceedings of the 2022 Conference of the North American Chapter of the …, 2022
152022
Human-computer question answering: The case for quizbowl
J Boyd-Graber, S Feng, P Rodriguez
The NIPS'17 Competition: Building Intelligent Systems, 169-180, 2018
122018
Llm evaluators recognize and favor their own generations
A Panickssery, SR Bowman, S Feng
arXiv preprint arXiv:2404.13076, 2024
82024
How pre-trained word representations capture commonsense physical comparisons
P Goel, S Feng, J Boyd-Graber
Proceedings of the First Workshop on Commonsense Inference in Natural …, 2019
82019
Large Language Models Help Humans Verify Truthfulness--Except When They Are Convincingly Wrong
C Si, N Goyal, ST Wu, C Zhao, S Feng, H Daumé III, J Boyd-Graber
arXiv preprint arXiv:2310.12558, 2023
62023
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