Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions. - Eric Wallace; Jordan L. Boyd-Graber (ACL (3), 2018)

Pathologies of Neural Models Make Interpretation Difficult. - Shi Feng 005; Eric Wallace; Alvin Grissom II; Mohit Iyyer; Pedro Rodriguez 001; Jordan L. Boyd-Graber (EMNLP, 2018)

Interpreting Neural Networks with Nearest Neighbors. - Eric Wallace; Shi Feng 005; Jordan L. Boyd-Graber (BlackboxNLP@EMNLP, 2018)

Right Answer for the Wrong Reason: Discovery and Mitigation. - Shi Feng 005; Eric Wallace; Mohit Iyyer; Pedro Rodriguez 001; Alvin Grissom II; Jordan L. Boyd-Graber (CoRR, 2018)

Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions. - Eric Wallace; Pedro Rodriguez 001; Shi Feng 005; Jordan L. Boyd-Graber (CoRR, 2018)

Misleading Failures of Partial-input Baselines. - Shi Feng 005; Eric Wallace; Jordan L. Boyd-Graber (ACL (1), 2019)

Compositional Questions Do Not Necessitate Multi-hop Reasoning. - Sewon Min; Eric Wallace; Sameer Singh 001; Matt Gardner 001; Hannaneh Hajishirzi; Luke Zettlemoyer (ACL (1), 2019)

Universal Adversarial Triggers for Attacking and Analyzing NLP. - Eric Wallace; Shi Feng 005; Nikhil Kandpal; Matt Gardner 001; Sameer Singh 001 (EMNLP/IJCNLP (1), 2019)

AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models. - Eric Wallace; Jens Tuyls; Junlin Wang; Sanjay Subramanian; Matt Gardner 001; Sameer Singh 001 (EMNLP/IJCNLP (3), 2019)

Do NLP Models Know Numbers? Probing Numeracy in Embeddings. - Eric Wallace; Yizhong Wang; Sujian Li; Sameer Singh 001; Matt Gardner 001 (EMNLP/IJCNLP (1), 2019)

Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation. - Sahil Singla 002; Eric Wallace; Shi Feng 005; Soheil Feizi (ICML, 2019)

Universal Adversarial Triggers for NLP. - Eric Wallace; Shi Feng 005; Nikhil Kandpal; Matt Gardner 001; Sameer Singh 001 (CoRR, 2019)

Trick Me If You Can: Human-in-the-loop Generation of Adversarial Question Answering Examples. - Eric Wallace; Pedro Rodriguez 001; Shi Feng 005; Ikuya Yamada; Jordan L. Boyd-Graber (Trans. Assoc. Comput. Linguistics, 2019)

Pretrained Transformers Improve Out-of-Distribution Robustness. - Dan Hendrycks; Xiaoyuan Liu; Eric Wallace; Adam Dziedzic; Rishabh Krishnan; Dawn Song (ACL, 2020)

Evaluating Models' Local Decision Boundaries via Contrast Sets. - Matt Gardner 001; Yoav Artzi; Victoria Basmova; Jonathan Berant; Ben Bogin; Sihao Chen; Pradeep Dasigi; Dheeru Dua; Yanai Elazar; Ananth Gottumukkala; Nitish Gupta; Hannaneh Hajishirzi; et al. (EMNLP (Findings), 2020)

AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts. - Taylor Shin; Yasaman Razeghi; Robert L. Logan IV; Eric Wallace; Sameer Singh 001 (EMNLP (1), 2020)

Interpreting Predictions of NLP Models. - Eric Wallace; Matt Gardner 001; Sameer Singh 001 (EMNLP (Tutorial Abstracts), 2020)

Imitation Attacks and Defenses for Black-box Machine Translation Systems. - Eric Wallace; Mitchell Stern; Dawn Song (EMNLP (1), 2020)

Gradient-based Analysis of NLP Models is Manipulable. - Junlin Wang; Jens Tuyls; Eric Wallace; Sameer Singh 001 (EMNLP (Findings), 2020)

Train Big, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers. - Zhuohan Li 001; Eric Wallace; Sheng Shen 001; Kevin Lin; Kurt Keutzer; Dan Klein 001; Joey Gonzalez (ICML, 2020)

Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers. - Zhuohan Li 001; Eric Wallace; Sheng Shen 001; Kevin Lin; Kurt Keutzer; Dan Klein 001; Joseph E. Gonzalez (CoRR, 2020)

Evaluating NLP Models via Contrast Sets. - Matt Gardner 001; Yoav Artzi; Victoria Basmova; Jonathan Berant; Ben Bogin; Sihao Chen; Pradeep Dasigi; Dheeru Dua; Yanai Elazar; Ananth Gottumukkala; Nitish Gupta; Hanna Hajishirzi; et al. (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 003; Emmanuel Stapf; Ahmad-Reza Sadeghi; Daniel Demmler; Huili Chen; et al. (CoRR, 2020)

Customizing Triggers with Concealed Data Poisoning. - Eric Wallace; Tony Z. Zhao; Shi Feng 005; Sameer Singh 001 (CoRR, 2020)

Calibrate Before Use: Improving Few-shot Performance of Language Models. - Zihao Zhao; Eric Wallace; Shi Feng 005; Dan Klein 001; Sameer Singh 001 (ICML, 2021)

Concealed Data Poisoning Attacks on NLP Models. - Eric Wallace; Tony Z. Zhao; Shi Feng 005; Sameer Singh 001 (NAACL-HLT, 2021)

Detoxifying Language Models Risks Marginalizing Minority Voices. - Albert Xu; Eshaan Pathak; Eric Wallace; Suchin Gururangan; Maarten Sap; Dan Klein 001 (NAACL-HLT, 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 (USENIX Security Symposium, 2021)

Calibrate Before Use: Improving Few-Shot Performance of Language Models. - Tony Z. Zhao; Eric Wallace; Shi Feng 005; Dan Klein 001; Sameer Singh 001 (CoRR, 2021)

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 001; Sebastian Riedel 001 (ACL (Findings), 2022)

Automated Crossword Solving. - Eric Wallace; Nicholas Tomlin; Albert Xu; Kevin Yang; Eshaan Pathak; Matthew L. Ginsberg; Dan Klein 001 (ACL (1), 2022)

Analyzing Dynamic Adversarial Training Data in the Limit. - Eric Wallace; Adina Williams; Robin Jia; Douwe Kiela (ACL (Findings), 2022)

Deduplicating Training Data Mitigates Privacy Risks in Language Models. - Nikhil Kandpal; Eric Wallace; Colin Raffel (ICML, 2022)

InCoder: A Generative Model for Code Infilling and Synthesis. - Daniel Fried; Armen Aghajanyan; Jessy Lin; Sida Wang 001; Eric Wallace; Freda Shi; Ruiqi Zhong; Scott Yih; Luke Zettlemoyer; Mike Lewis (ICLR, 2023)

Measuring Forgetting of Memorized Training Examples. - Matthew Jagielski; Om Thakkar 001; Florian Tramèr; Daphne Ippolito; Katherine Lee; Nicholas Carlini; Eric Wallace; Shuang Song 001; Abhradeep Guha Thakurta; Nicolas Papernot; Chiyuan Zhang (ICLR, 2023)

Large Language Models Struggle to Learn Long-Tail Knowledge. - Nikhil Kandpal; Haikang Deng; Adam Roberts; Eric Wallace; Colin Raffel (ICML, 2023)

Poisoning Language Models During Instruction Tuning. - Alexander Wan; Eric Wallace; Sheng Shen 001; Dan Klein 001 (ICML, 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 (USENIX Security Symposium, 2023)

The False Promise of Imitating Proprietary LLMs. - Arnav Gudibande; Eric Wallace; Charlie Snell; Xinyang Geng; Hao Liu 055; Pieter Abbeel; Sergey Levine; Dawn Song (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)

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)

What Evidence Do Language Models Find Convincing? - Alexander Wan; Eric Wallace; Dan Klein 001 (ACL (1), 2024)

The False Promise of Imitating Proprietary Language Models. - Arnav Gudibande; Eric Wallace; Charlie Snell; Xinyang Geng; Hao Liu 055; Pieter Abbeel; Sergey Levine; Dawn Song (ICLR, 2024)

SILO Language Models: Isolating Legal Risk In a Nonparametric Datastore. - Sewon Min; Suchin Gururangan; Eric Wallace; Weijia Shi; Hannaneh Hajishirzi; Noah A. Smith; Luke Zettlemoyer (ICLR, 2024)

Stealing part of a production language model. - Nicholas Carlini; Daniel Paleka; Krishnamurthy Dj Dvijotham; Thomas Steinke 002; Jonathan Hayase; A. Feder Cooper; Katherine Lee; Matthew Jagielski; Milad Nasr; Arthur Conmy; Eric Wallace; David Rolnick; et al. (ICML, 2024)

Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation. - Danny Halawi; Alexander Wei 001; Eric Wallace; Tony Tong Wang; Nika Haghtalab; Jacob Steinhardt (ICML, 2024)

Privacy Side Channels in Machine Learning Systems. - Edoardo Debenedetti; Giorgio Severi; Milad Nasr; Christopher A. Choquette-Choo; Matthew Jagielski; Eric Wallace; Nicholas Carlini; Florian Tramèr (USENIX Security Symposium, 2024)

The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions. - Eric Wallace; Kai Xiao; Reimar Leike; Lilian Weng; Johannes Heidecke; Alex Beutel (CoRR, 2024)

Predicting Emergent Capabilities by Finetuning. - Charlie Snell; Eric Wallace; Dan Klein 001; Sergey Levine (CoRR, 2024)

Deliberative Alignment: Reasoning Enables Safer Language Models. - Melody Y. Guan; Manas Joglekar; Eric Wallace; Saachi Jain; Boaz Barak; Alec Helyar; Rachel Dias; Andrea Vallone; Hongyu Ren; Jason Wei; Hyung Won Chung; Sam Toyer; et al. (CoRR, 2024)

OpenAI o1 System Card. - Aaron Jaech; Adam Kalai; Adam Lerer; Adam Richardson; Ahmed El-Kishky; Aiden Low; Alec Helyar; Aleksander Madry; Alex Beutel; Alex Carney; Alex Iftimie; Alex Karpenko; et al. (CoRR, 2024)

Unfamiliar Finetuning Examples Control How Language Models Hallucinate. - Katie Kang; Eric Wallace; Claire J. Tomlin; Aviral Kumar; Sergey Levine (NAACL (Long Papers), 2025)

Trading Inference-Time Compute for Adversarial Robustness. - Wojciech Zaremba; Evgenia Nitishinskaya; Boaz Barak; Stephanie Lin; Sam Toyer; Yaodong Yu; Rachel Dias; Eric Wallace; Kai Xiao; Johannes Heidecke; Amelia Glaese (CoRR, 2025)

Estimating Worst-Case Frontier Risks of Open-Weight LLMs. - Eric Wallace; Olivia Watkins; Miles Wang; Kai Chen; Chris Koch (CoRR, 2025)