フォロー
Shi Feng
Shi Feng
The George Washington University
確認したメール アドレス: gwu.edu - ホームページ
タイトル
引用先
引用先
年
Calibrate before use: Improving few-shot performance of language models
Z Zhao, E Wallace, S Feng, D Klein, S Singh
ICML 2021, 2021
13162021
Universal adversarial triggers for attacking and analyzing NLP
E Wallace, S Feng, N Kandpal, M Gardner, S Singh
EMNLP 2019, 2019
9382019
Pathologies of Neural Models Make Interpretations Difficult
S Feng, E Wallace, A Grissom II, M Iyyer, P Rodriguez, J Boyd-Graber
EMNLP 2018, 2018
3802018
Concealed data poisoning attacks on NLP models
E Wallace, TZ Zhao, S Feng, S Singh
NAACL 2021, 2020
1982020
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
TACL 2019, 2019
1942019
Active example selection for in-context learning
Y Zhang, S Feng, C Tan
EMNLP 2022, 2022
1692022
What can AI do for me? evaluating machine learning interpretations in cooperative play
S Feng, J Boyd-Graber
IUI 2019, 2019
1552019
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
1012016
LLM evaluators recognize and favor their own generations
A Panickssery, SR Bowman, S Feng
NeurIPS 2024, 2024
782024
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
76*2016
Interpreting neural networks with nearest neighbors
E Wallace, S Feng, J Boyd-Graber
BlackboxNLP @ EMNLP 2018, 2018
632018
Understanding impacts of high-order loss approximations and features in deep learning interpretation
S Singla, E Wallace, S Feng, S Feizi
ICML 2019, 2019
612019
Misleading failures of partial-input baselines
S Feng, E Wallace, J Boyd-Graber
ACL 2019, 2019
432019
Quizbowl: The case for incremental question answering
P Rodriguez, S Feng, M Iyyer, H He, J Boyd-Graber
arXiv preprint arXiv:1904.04792, 2019
422019
Measuring inductive biases of in-context learning with underspecified demonstrations
C Si, D Friedman, N Joshi, S Feng, D Chen, H He
ACL 2023, 2023
332023
Machine explanations and human understanding
C Chen, S Feng, A Sharma, C Tan
TMLR 2023, 2022
312022
Human-centered evaluation of explanations
J Boyd-Graber, S Carton, S Feng, QV Liao, T Lombrozo, A Smith-Renner, ...
NAACL 2022 Tutorial, 2022
212022
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
NAACL 2024, 2023
202023
Language models learn to mislead humans via rlhf
J Wen, R Zhong, A Khan, E Perez, J Steinhardt, M Huang, SR Bowman, ...
arXiv preprint arXiv:2409.12822, 2024
122024
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
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