SynthesizeMe! Inducing Persona-Guided Prompts for Personalized Reward
  Models in LLMs
Authors: name: Michael J Ryan, name: Omar Shaikh, name: Aditri Bhagirath, name: Daniel Frees, name: William Held, name: Diyi Yang

Recent calls for pluralistic alignment of Large Language Models (LLMs)
encourage adapting models to diverse user preferences. However, most prior work
on personalized reward models heavily rely on additional identity information,
such as demographic details or a predefined set of preference categories. To
this end, we introduce SynthesizeMe, an approach to inducing synthetic user
personas from user interactions for personalized reward modeling. SynthesizeMe
first generates and verifies reasoning to explain user preferences, then
induces synthetic user personas from that reasoning, and finally filters to
informative prior user interactions in order to build personalized prompts for
a particular user. We show that using SynthesizeMe induced prompts improves
personalized LLM-as-a-judge accuracy by 4.4% on Chatbot Arena. Combining
SynthesizeMe derived prompts with a reward model achieves top performance on
PersonalRewardBench: a new curation of user-stratified interactions with
chatbots collected from 854 users of Chatbot Arena and PRISM.