Patrick Chao

Hello! I am a final-year Statistics Ph.D. student at the University of Pennsylvania, gratefully advised by Edgar Dobriban, and collaborate with Eric Wong and Hamed Hassani. I am broadly interested in making language models smarter and safer.

For my undergraduate education, I graduated from UC Berkeley, receiving Bachelor's degrees in Computer Science, Mathematics, and Statistics with honors. I was very fortunate to have been advised by William Fithian and Horia Mania.

Previously, I interned at Amazon AI, working on diffusion models for causality, and at Jane Street Capital as a trading intern.

Email / Github / Google Scholar

Research

	JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models Patrick Chao, Edoardo Debenedetti, Alexander Robey, Maksym Andriushchenko, Francesco Croce, Vikash Sehwag, Edgar Dobriban, Nicolas Flammarion, George J. Pappas, Florian Tramer, Hamed Hassani, Eric Wong arXiv \| Leaderboard \| Github We introduce a benchmark, automated evaluation pipeline, and leaderboard for jailbreak attacks and defenses.
	A Safe Harbor for AI Evaluation and Red Teaming Shayne Longpre, Sayash Kapoor, Kevin Klyman, Ashwin Ramaswami, Rishi Bommasani, Borhane Blili-Hamelin, Yangsibo Huang, Aviya Skowron, Zheng-Xin Yong, Suhas Kotha, Yi Zeng, Weiyan Shi, Xianjun Yang, Reid Southen, Alexander Robey, Patrick Chao, Diyi Yang, Ruoxi Jia, Daniel Kang, Sandy Pentland, Arvind Narayanan, Percy Liang, Peter Henderson arXiv \| Website \| Blog We propose AI companies provide a safe harbor regarding evaluation and red teaming to promote safety, security, and trustworthiness of AI systems.
	Jailbreaking Black Box Large Language Models in Twenty Queries Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, Eric Wong NeurIPS Robustness of Few-shot Learning in Foundation Models 2023 arXiv \| Website \| Github \| VentureBeat We propose PAIR, an automated method that uses a language model to systematically generate semantic jailbreaks for other language models, often in under twenty queries. PAIR is more computationally efficient than state-of-the-art methods by many orders of magnitude and only requires black-box access.
	Statistical Estimation Under Distribution Shift: Wasserstein Perturbations and Minimax Theory Patrick Chao, Edgar Dobriban arXiv \| Github When estimating an average under distribution shifts, folk wisdom is to use something robust like the median. Surprisingly, under bounded (Wasserstein) shifts, we show that the sample mean remains optimal. For linear regression, we show that ordinary least squares remains optimal.
	Adversarial Prompting for Black Box Foundation Models Natalie Maus, Patrick Chao*, Eric Wong, Jacob Gardner ICML Frontiers in Adversarial Machine Learning Workshop 2023* arXiv \| Blog Post \| Github Systematically finding adversarial prompts for generative image models like Stable Diffusion and text models like GPT.
	Interventional and Counterfactual Inference with Diffusion Models Patrick Chao, Patrick Blöbaum, Shiva Kasiviswanathan ICML Counterfactuals in Minds & Machines Workshop 2023 (Oral) arXiv \| Github Using diffusion models to answer interventional and counterfactual queries by modeling observational data, achieving state-of-the-art performance.
	AdaPT-GMM: Powerful and Robust Covariate-Assisted Multiple Testing Patrick Chao, William Fithian arXiv \| Vignette \| Talk Recording \| Github A powerful and robust multiple testing method using covariates to model the local false discovery rate by fitting a Gaussian mixture model.
	Different Definitions of Conic Sections in Hyperbolic Geometry Patrick Chao, Jonathan Rosenberg Involve Research Journal, 2018 arXiv We show the standard definitions of conic sections are not equivalent in hyperbolic geometry and we define generalized versions.

Teaching

Teaching Assistant: Intro to Business Statistics (STAT 1010), Intro to Statistics (STAT 1110), Sports Analytics (STAT 4010), Data Collection and Acquisition (STAT 4100/7100), Python for Data Science (STAT 7770) (2020-2023)

Teaching Assistant, CS189: Machine Learning, Spring & Fall 2019

Teaching Assistant, Data 100: Principles and Techniques of Data Science, Fall 2018

Last updated Feb 2024.
Website template from Jon Barron.