Hello! I am a fourth-year PhD student at UC Berkeley working on machine learning and NLP. I am advised by Dan Klein and Dawn Song, and I am fortunate to also work with external collaborators such as Sameer Singh, Nicholas Carlini, and Colin Raffel. My research is supported by the Apple Scholars in AI Fellowship. Outside of Berkeley, I am a student researcher at Google Brain for 2023, and I have previously interned at FAIR and AI2.

Starting Fall 2023, I'm on the job market for academia and industry!

Hello! I am a fourth-year PhD student at UC Berkeley working on machine learning and NLP. I am advised by Dan Klein and Dawn Song, and I am fortunate to also work with external collaborators such as Sameer Singh, Nicholas Carlini, and Colin Raffel. My research is supported by the Apple Scholars in AI Fellowship. Outside of Berkeley, I am a student researcher at Google Brain for 2023, and I have previously interned at FAIR and AI2.

Starting Fall 2023, I'm on the job market for academia and industry!

Current Research Interests

I focus on improving language models, enhancing the security/privacy/reliability of ML, and the intersection of these topics. Some of my recent research directions include:

• Memorization & Privacy We've shown that LMs and diffusion models can memorize their training data [1,2,3,4], raising concerns regarding privacy, copyright agreements, GDPR statutes, and more.
• Prompting & Decoding We've done some of the early work on prompting LMs, including prompt design [4,5], parameter efficiency [6], and understanding failure modes [7].
• Robustness We've studied different types of natural [8] and adversarial distribution shifts [9,10,11], and we have traced model failures back to quality and diversity issues in the training data [12,13,14,15].
• New Threat Models We've explored and refined new types of adversarial vulnerabilities, including stealing models weights [16] and poisoning training sets [17].

Selected Publications

Here are some of my representative papers. See my Google Scholar page for a complete list.

• 

  Extracting Training Data from Diffusion Models

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

  arXiv preprint

  TLDR | Twitter | Paper| Citation

  TLDR: We show how to extract hundreds of memorized images from popular diffusion models like Imagen and Stable Diffusion.
  

  @article{carlini2023extracting,
            title={Extracting training data from diffusion models},
            author={Carlini, Nicholas and Hayes, Jamie and Nasr, Milad and Jagielski, Matthew and Sehwag, Vikash and Tram{\`e}r, Florian and Balle, Borja and Ippolito, Daphne and Wallace, Eric},
            journal={arXiv preprint arXiv:2301.13188},
            year={2023}}

• 

  Automated Crossword Solving

  Eric Wallace*, Nicholas Tomlin*, Albert Xu*, Kevin Yang*, Eshaan Pathak*, Matt Ginsberg, Dan Klein

  ACL 2022

  TLDR| Blog| Demo| Twitter| Paper| Code| Slides| Poster| Citation

  TLDR: We create an AI for solving crossword puzzles that outperforms the world's best human players.
  

  @inproceedings{Wallace2022Crosswords,  
      title={Automated Crossword Solving},
      author={Wallace, Eric and Tomlin, Nicholas and Xu, Albert and Yang, Kevin and Pathak, Eshaan and Ginsberg, Matthew L. and Klein, Dan}, 
      booktitle={Association for Computational Linguistics},
      year={2022}}

• 

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

  Tony Zhao*, Eric Wallace*, Shi Feng, Dan Klein, Sameer Singh

  ICML 2021. Oral Presentation, top 3%

  TLDR| Twitter #1#2| Paper| Code| Slides| Citation

  TLDR: We are the first to show that GPT-3's accuracy has high variance across different choices of the prompt. We propose a calibration procedure that reduces this variance and substantially improves average accuracy.
  

  @inproceedings{Zhao2021Calibrate,  
            Title = {Calibrate Before Use: Improving Few-shot Performance of Language Models},
            Author = {Tony Z. Zhao and Eric Wallace and Shi Feng and Dan Klein and Sameer Singh}, 
            booktitle={International Conference on Machine Learning},
            Year = {2021}}

• 

  Extracting Training Data From Large Language Models

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

  USENIX Security 2021

  TLDR| Blog| Twitter #1#2| Paper| Code| Citation

  TLDR: We create a method for extracting verbatim training examples from a language model.
  

  @inproceedings{carlini2020extracting,
              title={Extracting Training Data from Large Language Models},
              author={Nicholas Carlini and Florian Tram\`er and Eric Wallace and Matthew Jagielski 
               and Ariel Herbert-Voss and Katherine Lee and Adam Roberts and Tom Brown
               and Dawn Song and \'Ulfar Erlingsson and Alina Oprea and Colin Raffel},
              booktitle={USENIX Security Symposium},
              year={2021}}

• 

  AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts

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

  EMNLP 2020

  TLDR| Twitter| Paper| Code| Citation

  TLDR: We propose a method for automatically designing prompts for large language models.
  

  @inproceedings{Shin2020Autoprompt,
            Author = {Taylor Shin and Yasaman Razeghi and Robert L. Logan IV and Eric Wallace and Sameer Singh},    
            BookTitle={Empirical Methods in Natural Language Processing},
            Year = {2020},
            Title = {{AutoPrompt}: Eliciting Knowledge from Language Models with Automatically Generated Prompts}}

• 

  Universal Adversarial Triggers for Attacking and Analyzing NLP

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

  EMNLP 2019

  TLDR| Video| Blog| Twitter| Paper| Code| Slides| Citation

  TLDR: We create phrases that cause a model to produce a specific prediction when concatenated to any input. Triggers reveal egregious and insightful errors for text classification, reading comprehension, and text generation.
  

  @inproceedings{Wallace2019Triggers,
      Author = {Eric Wallace and Shi Feng and Nikhil Kandpal and Matt Gardner and Sameer Singh},
      Booktitle = {Empirical Methods in Natural Language Processing},
      Year = {2019},
      Title = {Universal Adversarial Triggers for Attacking and Analyzing {NLP}}}

• 

  AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models

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

  EMNLP 2019. Best Demo Award

  TLDR| Landing Page| Twitter| Demo| Paper| Poster| Citation

  TLDR: We build an open-source toolkit on top of AllenNLP that makes it easy to interpret NLP models.
  

  @inproceedings{Wallace2019AllenNLP,
                       Author = {Eric Wallace and Jens Tuyls and Junlin Wang and Sanjay Subramanian and Matt Gardner and Sameer Singh},
                       Booktitle = {Empirical Methods in Natural Language Processing},
                       Year = {2019},
                       Title = {{AllenNLP Interpret}: A Framework for Explaining Predictions of {NLP} Models}}

• 

  Pathologies of Neural Models Make Interpretations Difficult

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

  EMNLP 2018

  TLDR| Video| Paper| Slides| Code| Citation

  TLDR: Saliency maps, a popular interpretation technique, can be negatively impacted by certain pathological behavior present in neural models, such as prediction overconfidence.
  

  @inproceedings{Feng2018Pathological,
                      Author = {Shi Feng and Eric Wallace and Alvin Grissom II and Mohit Iyyer and Pedro Rodriguez and Jordan Boyd-Graber},
                      Booktitle = {Empirical Methods in Natural Language Processing},
                      Year = {2018},
                      Title = {Pathologies of Neural Models Make Interpretations Difficult}}

Teaching & Mentoring

I am passionate about mentoring and teaching. At the moment, I currently don't have capacity to advise new research projects, but I happy to answer questions via email about my work and research at Berkeley.

• 

  CS288: Natural Language Processing

  Spring 2023

  Co-taught with Dan Klein and Kevin Lin. In the second half of the course, I covered many cutting-edge topics including LM scaling, systems, risks, RLHF, retrieval, and more.

  Slides| Finetuning HW| Finetuning HW

• 

  Interpreting Predictions of NLP Models

  EMNLP 2020

  Sameer Singh, Matt Gardner, and I gave a tutorial on methods for interpreting the predictions and behavior of NLP and language models.

  Slides| Website

Selected Media Coverage

Here are some news articles that feature my work, including interviews with me or my colleagues.

• What a Crossword AI Reveals About Humans

• Privacy & Security for Diffusion and LMs

• What does GPT-3 “know” about me?

• Neil deGrasse Tyson Podcast (Crosswords)

• Does GPT-2 Know Your Phone Number?

• AI models spit out photos of people and copyrighted images

• Privacy Considerations in Language Models

• Neural Crossword Solver Outperforms Humans For First Time