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Dropping Just a Handful of Preferences Can Change Top Large Language Model Rankings
We present a method for auditing the robustness of LLM ranking systems to worst-case data-dropping; we find that dropping just 0.02% of human (and AI) preferences can change the top-ranked models on Chatbot Arena.
Jenny Y. Huang*, Yunyi Shen*, Dennis Wei, Tamara Broderick
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Approximations to Worst-Case Data-dropping: Unmasking Failure Modes
A data analyst might worry about generalization if dropping a very small fraction of data points from a study could change its …
Jenny Y. Huang, David R. Burt, Tin D. Nguyen, Yunyi Shen, Tamara Broderick
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Detecting Changes in the Transmission Rate of a Stochastic Epidemic Model
Throughout the course of an epidemic, the rate at which disease spreads varies with behavioral changes, the emergence of new disease …
Jenny Y. Huang, Raphaël Morsomme, David Dunson, Jason Xu
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Detecting Changes in the Transmission Rate of a Stochastic Epidemic Model
Moving towards a more equal world, one ride at a time: Studying Public Transportation Initiatives using interpretable causal inference
The goal of low-income fare subsidy programs is to increase equitable access to public transit, and in doing so, increase access to …
Gaurav Parikh*, Jenny Huang*, Albert Sun*, Lesia Semenova, and Cynthia Rudin
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Selective sweeps in SARS-CoV-2 variant competition
The main mathematical result in this paper is that change of variables in the ordinary differential equation (ODE) for the competition …
Laura Boyle, Sofia Hletko, Jenny Huang, June Lee, Gaurav Pallod, Hwai-Ray Tung, and Richard Durrett
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Selective sweeps in SARS-CoV-2 variant competition