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Shi Feng
Shi Feng
The George Washington University
Dirección de correo verificada de gwu.edu - Página principal
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Año
Calibrate before use: Improving few-shot performance of language models
Z Zhao, E Wallace, S Feng, D Klein, S Singh
ICML 2021, 2021
11162021
Universal adversarial triggers for attacking and analyzing NLP
E Wallace, S Feng, N Kandpal, M Gardner, S Singh
EMNLP 2019, 2019
8402019
Pathologies of Neural Models Make Interpretations Difficult
S Feng, E Wallace, A Grissom II, M Iyyer, P Rodriguez, J Boyd-Graber
EMNLP 2018, 2018
3612018
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
1732019
Concealed data poisoning attacks on NLP models
E Wallace, TZ Zhao, S Feng, S Singh
NAACL 2021, 2020
165*2020
What can AI do for me? evaluating machine learning interpretations in cooperative play
S Feng, J Boyd-Graber
IUI 2019, 2019
1502019
Active example selection for in-context learning
Y Zhang, S Feng, C Tan
EMNLP 2022, 2022
1252022
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
942016
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
BlackboxNLP @ EMNLP 2018, 2018
612018
Understanding impacts of high-order loss approximations and features in deep learning interpretation
S Singla, E Wallace, S Feng, S Feizi
ICML 2019, 2019
572019
Misleading failures of partial-input baselines
S Feng, E Wallace, J Boyd-Graber
ACL 2019, 2019
412019
Quizbowl: The case for incremental question answering
P Rodriguez, S Feng, M Iyyer, H He, J Boyd-Graber
arXiv preprint arXiv:1904.04792, 2019
402019
LLM evaluators recognize and favor their own generations
A Panickssery, SR Bowman, S Feng
NeurIPS 2024, 2024
322024
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
242023
Machine explanations and human understanding
C Chen, S Feng, A Sharma, C Tan
TMLR 2023, 2022
222022
Human-centered evaluation of explanations
J Boyd-Graber, S Carton, S Feng, QV Liao, T Lombrozo, A Smith-Renner, ...
NAACL 2022 Tutorial, 2022
182022
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
142023
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
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
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Artículos 1–20