Feng, Shi

Universal Adversarial Triggers for Attacking and Analyzing NLP

Wallace, E., Feng, S., Kandpal, N., Gardner, M., & Singh, S. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 642

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

Zhao, T. Z., Wallace, E., Feng, S., Klein, D., & Singh, S. (2021) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 513

Multimodal Sentiment Detection Based on Multi-channel Graph Neural Networks

Yang, X., Feng, S., Zhang, Y., & Wang, D. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Sentiment Analysis and Opinion MiningMultimodal Machine Learning Applications

Cited by 145

Concealed Data Poisoning Attacks on NLP Models

Wallace, E., Zhao, T., Feng, S., & Singh, S. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 115

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

Si, C., Goyal, N., Wu, T., Zhao, C., Feng, S., Daumé Iii, H., & Boyd-Graber, J. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Misinformation and Its ImpactsTopic Modeling

Cited by 26

Language Models Learn to Mislead Humans via RLHF

Wen, J., Zhong, R., Khan, A., Perez, E., Steinhardt, J., Huang, M., Bowman, S. R., He, H., & Feng, S. (2024) · arXiv

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 4

Unsupervised Elicitation of Language Models

Wen, J., Ankner, Z., Somani, A., Hase, P., Marks, S., Goldman-Wetzler, J., Petrini, L., Sleight, H., Burns, C., He, H., et al. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0