6 citations · 13 across the 6 of their papers we have counts for
6 papers
Preference learning made easy: Everything should be understood through win rate
Lily H. Zhang, Rajesh Ranganath
Preference learning, or the task of aligning generative models to preference comparison data, has yet to reach the conceptual maturity of classification, density estimation, etc. T…
Towards Minimal Targeted Updates of Language Models with Targeted Negative Training
Lily H. Zhang, Rajesh Ranganath, Arya Tafvizi
Generative models of language exhibit impressive capabilities but still place non-negligible probability mass over undesirable outputs. In this work, we address the task of updatin…
Preference Learning Algorithms Do Not Learn Preference Rankings
Angelica Chen, Sadhika Malladi, Lily H. Zhang +4
Preference learning algorithms (e.g., RLHF and DPO) are frequently used to steer LLMs to produce generations that are more preferred by humans, but our understanding of their inner…
When accurate prediction models yield harmful self-fulfilling prophecies
Wouter A. C. van Amsterdam, Nan van Geloven, Jesse H. Krijthe +2
Prediction models are popular in medical research and practice. By predicting an outcome of interest for specific patients, these models may help inform difficult treatment decisio…
Stochastic interpolants with data-dependent couplings
Michael S. Albergo, Mark Goldstein, Nicholas M. Boffi +2
Generative models inspired by dynamical transport of measure -- such as flows and diffusions -- construct a continuous-time map between two probability densities. Conventionally, o…
When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlations
Rhys Compton, Lily Zhang, Aahlad Puli +1
In machine learning, incorporating more data is often seen as a reliable strategy for improving model performance; this work challenges that notion by demonstrating that the additi…