Eric Wallace

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 72

Top Topics

Machine Learning Systems

Question Answering

Top Venues

Publications

Alexander Wan, Eric Wallace, Dan Klein
What Evidence Do Language Models Find Convincing? ACL (1) (2024)
Danny Halawi, Alexander Wei, Eric Wallace, Tony T. Wang, Nika Haghtalab, Jacob Steinhardt
Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation. CoRR (2024)
Alexander Wan, Eric Wallace, Dan Klein
What Evidence Do Language Models Find Convincing? CoRR (2024)
Katie Kang, Eric Wallace, Claire J. Tomlin, Aviral Kumar, Sergey Levine
Unfamiliar Finetuning Examples Control How Language Models Hallucinate. CoRR (2024)
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
Stealing Part of a Production Language Model. CoRR (2024)
Eric Wallace, Kai Xiao, Reimar Leike, Lilian Weng, Johannes Heidecke, Alex Beutel
The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions. CoRR (2024)
Edoardo Debenedetti, Giorgio Severi, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Eric Wallace, Nicholas Carlini, Florian Tramèr
Privacy Side Channels in Machine Learning Systems. USENIX Security Symposium (2024)
Arnav Gudibande, Eric Wallace, Charlie Snell, Xinyang Geng, Hao Liu, Pieter Abbeel, Sergey Levine, Dawn Song
The False Promise of Imitating Proprietary Language Models. ICLR (2024)
Sewon Min, Suchin Gururangan, Eric Wallace, Weijia Shi, Hannaneh Hajishirzi, Noah A. Smith, Luke Zettlemoyer
SILO Language Models: Isolating Legal Risk In a Nonparametric Datastore. ICLR (2024)
Alexander Wan, Eric Wallace, Sheng Shen, Dan Klein
Poisoning Language Models During Instruction Tuning. ICML (2023)
Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, Eric Wallace
Extracting Training Data from Diffusion Models. USENIX Security Symposium (2023)
Arnav Gudibande, Eric Wallace, Charlie Snell, Xinyang Geng, Hao Liu, Pieter Abbeel, Sergey Levine, Dawn Song
The False Promise of Imitating Proprietary LLMs. CoRR (2023)
Milad Nasr, Nicholas Carlini, Jonathan Hayase, Matthew Jagielski, A. Feder Cooper, Daphne Ippolito, Christopher A. Choquette-Choo, Eric Wallace, Florian Tramèr, Katherine Lee
Scalable Extraction of Training Data from (Production) Language Models. CoRR (2023)
Sewon Min, Suchin Gururangan, Eric Wallace, Hannaneh Hajishirzi, Noah A. Smith, Luke Zettlemoyer
SILO Language Models: Isolating Legal Risk In a Nonparametric Datastore. CoRR (2023)
Alexander Wan, Eric Wallace, Sheng Shen, Dan Klein
Poisoning Language Models During Instruction Tuning. CoRR (2023)
Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, Eric Wallace
Extracting Training Data from Diffusion Models. CoRR (2023)
Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Scott Yih, Luke Zettlemoyer, Mike Lewis
InCoder: A Generative Model for Code Infilling and Synthesis. ICLR (2023)
Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, Colin Raffel
Large Language Models Struggle to Learn Long-Tail Knowledge. ICML (2023)
Edoardo Debenedetti, Giorgio Severi, Nicholas Carlini, Christopher A. Choquette-Choo, Matthew Jagielski, Milad Nasr, Eric Wallace, Florian Tramèr
Privacy Side Channels in Machine Learning Systems. CoRR (2023)
Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Guha Thakurta, Nicolas Papernot, Chiyuan Zhang
Measuring Forgetting of Memorized Training Examples. ICLR (2023)
Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Chiyuan Zhang
Measuring Forgetting of Memorized Training Examples. CoRR (2022)
Eric Wallace, Adina Williams, Robin Jia, Douwe Kiela
Analyzing Dynamic Adversarial Training Data in the Limit. ACL (Findings) (2022)
Nikhil Kandpal, Eric Wallace, Colin Raffel
Deduplicating Training Data Mitigates Privacy Risks in Language Models. ICML (2022)
Robert L. Logan IV, Ivana Balazevic, Eric Wallace, Fabio Petroni, Sameer Singh, Sebastian Riedel
Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models. ACL (Findings) (2022)
Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer, Mike Lewis
InCoder: A Generative Model for Code Infilling and Synthesis. CoRR (2022)
Eric Wallace, Nicholas Tomlin, Albert Xu, Kevin Yang, Eshaan Pathak, Matthew Ginsberg, Dan Klein
Automated Crossword Solving. CoRR (2022)
Nikhil Kandpal, Eric Wallace, Colin Raffel
Deduplicating Training Data Mitigates Privacy Risks in Language Models. CoRR (2022)
Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, Colin Raffel
Large Language Models Struggle to Learn Long-Tail Knowledge. CoRR (2022)
Eric Wallace, Nicholas Tomlin, Albert Xu, Kevin Yang, Eshaan Pathak, Matthew Ginsberg, Dan Klein
Automated Crossword Solving. ACL (1) (2022)
Albert Xu, Eshaan Pathak, Eric Wallace, Suchin Gururangan, Maarten Sap, Dan Klein
Detoxifying Language Models Risks Marginalizing Minority Voices. CoRR (2021)
Eric Wallace, Tony Z. Zhao, Shi Feng, Sameer Singh
Concealed Data Poisoning Attacks on NLP Models. NAACL-HLT (2021)
Eric Wallace, Adina Williams, Robin Jia, Douwe Kiela
Analyzing Dynamic Adversarial Training Data in the Limit. CoRR (2021)
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, Sameer Singh
Calibrate Before Use: Improving Few-shot Performance of Language Models. ICML (2021)
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
Extracting Training Data from Large Language Models. USENIX Security Symposium (2021)
Robert L. Logan IV, Ivana Balazevic, Eric Wallace, Fabio Petroni, Sameer Singh, Sebastian Riedel
Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models. CoRR (2021)
Tony Z. Zhao, Eric Wallace, Shi Feng, Dan Klein, Sameer Singh
Calibrate Before Use: Improving Few-Shot Performance of Language Models. CoRR (2021)
Albert Xu, Eshaan Pathak, Eric Wallace, Suchin Gururangan, Maarten Sap, Dan Klein
Detoxifying Language Models Risks Marginalizing Minority Voices. NAACL-HLT (2021)
Junlin Wang, Jens Tuyls, Eric Wallace, Sameer Singh
Gradient-based Analysis of NLP Models is Manipulable. CoRR (2020)
Eric Wallace, Mitchell Stern, Dawn Song
Imitation Attacks and Defenses for Black-box Machine Translation Systems. CoRR (2020)
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
Trustworthy AI Inference Systems: An Industry Research View. CoRR (2020)
Eric Wallace, Mitchell Stern, Dawn Song
Imitation Attacks and Defenses for Black-box Machine Translation Systems. EMNLP (1) (2020)
Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, Adam Dziedzic, Rishabh Krishnan, Dawn Song
Pretrained Transformers Improve Out-of-Distribution Robustness. CoRR (2020)
Junlin Wang, Jens Tuyls, Eric Wallace, Sameer Singh
Gradient-based Analysis of NLP Models is Manipulable. EMNLP (Findings) (2020)
Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, Adam Dziedzic, Rishabh Krishnan, Dawn Song
Pretrained Transformers Improve Out-of-Distribution Robustness. ACL (2020)
Eric Wallace, Matt Gardner, Sameer Singh
Interpreting Predictions of NLP Models. EMNLP (Tutorial Abstracts) (2020)
Eric Wallace, Tony Z. Zhao, Shi Feng, Sameer Singh
Customizing Triggers with Concealed Data Poisoning. CoRR (2020)
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, Sameer Singh
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts. EMNLP (1) (2020)
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
Evaluating NLP Models via Contrast Sets. CoRR (2020)
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
Extracting Training Data from Large Language Models. CoRR (2020)
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, Sameer Singh
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts. CoRR (2020)
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
Evaluating Models' Local Decision Boundaries via Contrast Sets. EMNLP (Findings) (2020)
Zhuohan Li, Eric Wallace, Sheng Shen, Kevin Lin, Kurt Keutzer, Dan Klein, Joey Gonzalez
Train Big, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers. ICML (2020)
Zhuohan Li, Eric Wallace, Sheng Shen, Kevin Lin, Kurt Keutzer, Dan Klein, Joseph E. Gonzalez
Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers. CoRR (2020)
Shi Feng, Eric Wallace, Jordan L. Boyd-Graber
Misleading Failures of Partial-input Baselines. ACL (1) (2019)
Eric Wallace, Yizhong Wang, Sujian Li, Sameer Singh, Matt Gardner
Do NLP Models Know Numbers? Probing Numeracy in Embeddings. CoRR (2019)
Eric Wallace, Pedro Rodriguez, Shi Feng, Ikuya Yamada, Jordan L. Boyd-Graber
Trick Me If You Can: Human-in-the-loop Generation of Adversarial Question Answering Examples. Trans. Assoc. Comput. Linguistics 7 (2019)
Sewon Min, Eric Wallace, Sameer Singh, Matt Gardner, Hannaneh Hajishirzi, Luke Zettlemoyer
Compositional Questions Do Not Necessitate Multi-hop Reasoning. CoRR (2019)
Eric Wallace, Jens Tuyls, Junlin Wang, Sanjay Subramanian, Matt Gardner, Sameer Singh
AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models. EMNLP/IJCNLP (3) (2019)
Eric Wallace, Jens Tuyls, Junlin Wang, Sanjay Subramanian, Matt Gardner, Sameer Singh
AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models. CoRR (2019)
Eric Wallace, Yizhong Wang, Sujian Li, Sameer Singh, Matt Gardner
Do NLP Models Know Numbers? Probing Numeracy in Embeddings. EMNLP/IJCNLP (1) (2019)
Sahil Singla, Eric Wallace, Shi Feng, Soheil Feizi
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation. ICML (2019)
Sahil Singla, Eric Wallace, Shi Feng, Soheil Feizi
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation. CoRR (2019)
Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, Sameer Singh
Universal Adversarial Triggers for Attacking and Analyzing NLP. EMNLP/IJCNLP (1) (2019)
Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, Sameer Singh
Universal Adversarial Triggers for NLP. CoRR (2019)
Sewon Min, Eric Wallace, Sameer Singh, Matt Gardner, Hannaneh Hajishirzi, Luke Zettlemoyer
Compositional Questions Do Not Necessitate Multi-hop Reasoning. ACL (1) (2019)
Shi Feng, Eric Wallace, Jordan L. Boyd-Graber
Misleading Failures of Partial-input Baselines. CoRR (2019)
Shi Feng, Eric Wallace, Alvin Grissom II, Mohit Iyyer, Pedro Rodriguez, Jordan L. Boyd-Graber
Pathologies of Neural Models Make Interpretation Difficult. EMNLP (2018)
Eric Wallace, Shi Feng, Jordan L. Boyd-Graber
Interpreting Neural Networks With Nearest Neighbors. CoRR (2018)
Shi Feng, Eric Wallace, Mohit Iyyer, Pedro Rodriguez, Alvin Grissom II, Jordan L. Boyd-Graber
Right Answer for the Wrong Reason: Discovery and Mitigation. CoRR (2018)
Eric Wallace, Shi Feng, Jordan L. Boyd-Graber
Interpreting Neural Networks with Nearest Neighbors. BlackboxNLP@EMNLP (2018)
Eric Wallace, Pedro Rodriguez, Shi Feng, Jordan L. Boyd-Graber
Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions. CoRR (2018)
Eric Wallace, Jordan L. Boyd-Graber
Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions. ACL (3) (2018)