Eric Wallace

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Hello! I am a fourth-year PhD student at UC Berkeley working on machine learning and natural language processing. I am advised by Dan Klein and Dawn Song, and I have affiliations with BAIR, Berkeley NLP, and Berkeley Security. My research is supported by the Apple Scholars in AI Fellowship. In the past, I've interned at FAIR, AI2, and I did my undergrad at the University of Maryland.


I focus on large language models, security/privacy/trustworthiness in ML, and the intersection of these topics. Some of the directions that my collaborators and I have worked on include:

  →Memorization & Privacy We've shown that language models have a tendency to memorize and regenerate their training data [1,2,3], raising concerns regarding user privacy, copyright agreements, GDPR statutes, and more.

  →Prompting We've done some of the early work on "prompting" language models to solve tasks, including methods for prompt design [4,5], parameter efficiency [6], and understanding prompting failure modes [7].

  →Robustness We've demonstrated that NLP systems lack robustness to natural [8] and adversarial distribution shifts [9,10,11], and we have attributed these failures 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 for NLP systems, including ways to steal models weights [16] and poison training sets [17].