Eric Wallace

According to our database, Eric Wallace authored at least 45 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Stealing Part of a Production Language Model.

Nicholas Carlini, Daniel Paleka, Krishnamurthy (Dj) Dvijotham, Thomas Steinke, Jonathan Hayase, A. Feder Cooper, Katherine Lee, Matthew Jagielski, Milad Nasr, Arthur Conmy, Eric Wallace, David Rolnick, Florian Tramèr

CoRR, 2024

Unfamiliar Finetuning Examples Control How Language Models Hallucinate.

Katie Kang, Eric Wallace, Claire J. Tomlin, Aviral Kumar, Sergey Levine

CoRR, 2024

What Evidence Do Language Models Find Convincing?

Alexander Wan, Eric Wallace, Dan Klein

CoRR, 2024

2023

Scalable Extraction of Training Data from (Production) Language Models.

Milad Nasr, Nicholas Carlini, Jonathan Hayase, Matthew Jagielski, A. Feder Cooper, Daphne Ippolito, Christopher A. Choquette-Choo, Eric Wallace, Florian Tramèr, Katherine Lee

CoRR, 2023

Privacy Side Channels in Machine Learning Systems.

Edoardo Debenedetti, Giorgio Severi, Nicholas Carlini, Christopher A. Choquette-Choo, Matthew Jagielski, Milad Nasr, Eric Wallace, Florian Tramèr

CoRR, 2023

SILO Language Models: Isolating Legal Risk In a Nonparametric Datastore.

Sewon Min, Suchin Gururangan, Eric Wallace, Hannaneh Hajishirzi, Noah A. Smith, Luke Zettlemoyer

CoRR, 2023

The False Promise of Imitating Proprietary LLMs.

Arnav Gudibande, Eric Wallace, Charlie Snell, Xinyang Geng, Hao Liu, Pieter Abbeel, Sergey Levine, Dawn Song

CoRR, 2023

Extracting Training Data from Diffusion Models.

Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, Eric Wallace

Proceedings of the 32nd USENIX Security Symposium, 2023

Poisoning Language Models During Instruction Tuning.

Alexander Wan, Eric Wallace, Sheng Shen, Dan Klein

Proceedings of the International Conference on Machine Learning, 2023

Large Language Models Struggle to Learn Long-Tail Knowledge.

Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, Colin Raffel

Proceedings of the International Conference on Machine Learning, 2023

Measuring Forgetting of Memorized Training Examples.

Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Guha Thakurta, Nicolas Papernot, Chiyuan Zhang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

InCoder: A Generative Model for Code Infilling and Synthesis.

Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Scott Yih, Luke Zettlemoyer, Mike Lewis

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Deduplicating Training Data Mitigates Privacy Risks in Language Models.

Nikhil Kandpal, Eric Wallace, Colin Raffel

Proceedings of the International Conference on Machine Learning, 2022

Analyzing Dynamic Adversarial Training Data in the Limit.

Eric Wallace, Adina Williams, Robin Jia, Douwe Kiela

Proceedings of the Findings of the Association for Computational Linguistics: ACL 2022, 2022

Automated Crossword Solving.

Eric Wallace, Nicholas Tomlin, Albert Xu, Kevin Yang, Eshaan Pathak, Matthew L. Ginsberg, Dan Klein

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022

Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models.

Robert L. Logan IV, Ivana Balazevic, Eric Wallace, Fabio Petroni, Sameer Singh, Sebastian Riedel

Proceedings of the Findings of the Association for Computational Linguistics: ACL 2022, 2022

2021

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

Tony Z. Zhao, Eric Wallace, Shi Feng, Dan Klein, Sameer Singh

CoRR, 2021

Extracting Training Data from Large Language Models.

Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B. Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, Colin Raffel

Proceedings of the 30th USENIX Security Symposium, 2021

Detoxifying Language Models Risks Marginalizing Minority Voices.

Albert Xu, Eshaan Pathak, Eric Wallace, Suchin Gururangan, Maarten Sap, Dan Klein

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

Concealed Data Poisoning Attacks on NLP Models.

Eric Wallace, Tony Z. Zhao, Shi Feng, Sameer Singh

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

Calibrate Before Use: Improving Few-shot Performance of Language Models.

Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, Sameer Singh

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Customizing Triggers with Concealed Data Poisoning.

Eric Wallace, Tony Z. Zhao, Shi Feng, Sameer Singh

CoRR, 2020

Trustworthy AI Inference Systems: An Industry Research View.

Rosario Cammarota, Matthias Schunter, Anand Rajan, Fabian Boemer, Ágnes Kiss, Amos Treiber, Christian Weinert, Thomas Schneider, Emmanuel Stapf, Ahmad-Reza Sadeghi, Daniel Demmler, Huili Chen, Siam Umar Hussain, M. Sadegh Riazi, Farinaz Koushanfar, Saransh Gupta, Tajana Simunic Rosing, Kamalika Chaudhuri, Hamid Nejatollahi, Nikil D. Dutt, Mohsen Imani, Kim Laine, Anuj Dubey, Aydin Aysu, Fateme Sadat Hosseini, Chengmo Yang, Eric Wallace, Pamela Norton

CoRR, 2020

Evaluating NLP Models via Contrast Sets.

Matt Gardner, Yoav Artzi, Victoria Basmova, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hanna Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, Ben Zhou

CoRR, 2020

Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers.

Zhuohan Li, Eric Wallace, Sheng Shen, Kevin Lin, Kurt Keutzer, Dan Klein, Joseph E. Gonzalez

CoRR, 2020

Train Big, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers.

Zhuohan Li, Eric Wallace, Sheng Shen, Kevin Lin, Kurt Keutzer, Dan Klein, Joey Gonzalez

Proceedings of the 37th International Conference on Machine Learning, 2020

Gradient-based Analysis of NLP Models is Manipulable.

Junlin Wang, Jens Tuyls, Eric Wallace, Sameer Singh

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2020, 2020

Imitation Attacks and Defenses for Black-box Machine Translation Systems.

Eric Wallace, Mitchell Stern, Dawn Song

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020

Interpreting Predictions of NLP Models.

Eric Wallace, Matt Gardner, Sameer Singh

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts, 2020

AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts.

Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, Sameer Singh

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020

Evaluating Models' Local Decision Boundaries via Contrast Sets.

Matt Gardner, Yoav Artzi, Victoria Basmova, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, Ben Zhou

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2020, 2020

Pretrained Transformers Improve Out-of-Distribution Robustness.

Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, Adam Dziedzic, Rishabh Krishnan, Dawn Song

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020

2019

Trick Me If You Can: Human-in-the-loop Generation of Adversarial Question Answering Examples.

Eric Wallace, Pedro Rodriguez, Shi Feng, Ikuya Yamada, Jordan L. Boyd-Graber

Trans. Assoc. Comput. Linguistics, 2019

Universal Adversarial Triggers for NLP.

Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, Sameer Singh

CoRR, 2019

Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation.

Sahil Singla, Eric Wallace, Shi Feng, Soheil Feizi

Proceedings of the 36th International Conference on Machine Learning, 2019

Do NLP Models Know Numbers? Probing Numeracy in Embeddings.

Eric Wallace, Yizhong Wang, Sujian Li, Sameer Singh, Matt Gardner

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019

AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models.

Eric Wallace, Jens Tuyls, Junlin Wang, Sanjay Subramanian, Matt Gardner, Sameer Singh

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019

Universal Adversarial Triggers for Attacking and Analyzing NLP.

Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, Sameer Singh

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019

Compositional Questions Do Not Necessitate Multi-hop Reasoning.

Sewon Min, Eric Wallace, Sameer Singh, Matt Gardner, Hannaneh Hajishirzi, Luke Zettlemoyer

Proceedings of the 57th Conference of the Association for Computational Linguistics, 2019

Misleading Failures of Partial-input Baselines.

Shi Feng, Eric Wallace, Jordan L. Boyd-Graber

Proceedings of the 57th Conference of the Association for Computational Linguistics, 2019

2018

Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions.

Eric Wallace, Pedro Rodriguez, Shi Feng, Jordan L. Boyd-Graber

CoRR, 2018

Right Answer for the Wrong Reason: Discovery and Mitigation.

Shi Feng, Eric Wallace, Mohit Iyyer, Pedro Rodriguez, Alvin Grissom II, Jordan L. Boyd-Graber

CoRR, 2018

Interpreting Neural Networks with Nearest Neighbors.

Eric Wallace, Shi Feng, Jordan L. Boyd-Graber

Proceedings of the Workshop: Analyzing and Interpreting Neural Networks for NLP, 2018

Pathologies of Neural Models Make Interpretation Difficult.

Shi Feng, Eric Wallace, Alvin Grissom II, Mohit Iyyer, Pedro Rodriguez, Jordan L. Boyd-Graber

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31, 2018

Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions.

Eric Wallace, Jordan L. Boyd-Graber

Proceedings of ACL 2018, Melbourne, Australia, July 15-20, 2018, Student Research Workshop, 2018