| AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts T Shin, Y Razeghi, RL Logan IV, E Wallace, S Singh EMNLP 2020, 2020 | 653 | 2020 |
| Extracting Training Data from Large Language Models N Carlini, F Tramer, E Wallace, M Jagielski, A Herbert-Voss, K Lee, ... USENIX Security 2021, 2020 | 607 | 2020 |
| Universal Adversarial Triggers for Attacking and Analyzing NLP E Wallace, S Feng, N Kandpal, M Gardner, S Singh EMNLP 2019, 2019 | 503 | 2019 |
| Calibrate Before Use: Improving Few-Shot Performance of Language Models TZ Zhao, E Wallace, S Feng, D Klein, S Singh ICML 2021, 2021 | 359 | 2021 |
| Evaluating Models' Local Decision Boundaries via Contrast Sets M Gardner, Y Artzi, V Basmova, J Berant, B Bogin, S Chen, P Dasigi, ... EMNLP Findings 2020, 2020 | 325 | 2020 |
| Pretrained Transformers Improve Out-of-Distribution Robustness D Hendrycks, X Liu, E Wallace, A Dziedzic, R Krishnan, D Song ACL 2020, 2020 | 282 | 2020 |
| Pathologies of Neural Models Make Interpretations Difficult S Feng, E Wallace, II Grissom, M Iyyer, P Rodriguez, J Boyd-Graber EMNLP 2018, 2018 | 261 | 2018 |
| Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... arXiv preprint arXiv:2206.04615, 2022 | 203 | 2022 |
| Do NLP Models Know Numbers? Probing Numeracy in Embeddings E Wallace, Y Wang, S Li, S Singh, M Gardner EMNLP 2019, 2019 | 203 | 2019 |
| Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers Z Li, E Wallace, S Shen, K Lin, K Keutzer, D Klein, JE Gonzalez ICML 2020, 2020 | 188 | 2020 |
| Compositional Questions Do Not Necessitate Multi-hop Reasoning S Min, E Wallace, S Singh, M Gardner, H Hajishirzi, L Zettlemoyer ACL 2019, 2019 | 127 | 2019 |
| Trick Me If You Can: Human-in-the-loop Generation of Adversarial Question Answering Examples E Wallace, P Rodriguez, S Feng, I Yamada, J Boyd-Graber TACL 2019, 2019 | 124* | 2019 |
| InCoder: A Generative Model for Code Infilling and Synthesis D Fried, A Aghajanyan, J Lin, S Wang, E Wallace, F Shi, R Zhong, W Yih, ... ICLR 2023, 2022 | 112* | 2022 |
| AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models E Wallace, J Tuyls, J Wang, S Subramanian, M Gardner, S Singh EMNLP Demo 2019, Best Paper Award, 2019 | 108 | 2019 |
| Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models RL Logan IV, I Balažević, E Wallace, F Petroni, S Singh, S Riedel ACL Findings 2022, 2021 | 82 | 2021 |
| Concealed Data Poisoning Attacks on NLP Models E Wallace, TZ Zhao, S Feng, S Singh NAACL 2021, 2020 | 73* | 2020 |
| Imitation Attacks and Defenses for Black-box Machine Translation Systems E Wallace, M Stern, D Song EMNLP 2020, 2020 | 66 | 2020 |
| Extracting Training Data from Diffusion Models N Carlini, J Hayes, M Nasr, M Jagielski, V Sehwag, F Tramer, B Balle, ... arXiv preprint arXiv:2301.13188, 2023 | 51 | 2023 |
| Detoxifying Language Models Risks Marginalizing Minority Voices A Xu, E Pathak, E Wallace, S Gururangan, M Sap, D Klein NAACL 2021, 2021 | 51 | 2021 |
| Deduplicating Training Data Mitigates Privacy Risks in Language Models N Kandpal, E Wallace, C Raffel ICML 2022, 2022 | 43 | 2022 |
Sameer SinghAssociate Professor, University of California IrvineVerified email at uci.edu
