Agentic-Priors: Using Large Language Models to Address the Cold Start Problem in Preference Elicitation
Read the original on arXiv AI →Agentic-Priors uses large language models to generate informative priors for preference learning agents, addressing the cold start problem by translating a new user's trade-offs into a prior over multinomial logit coefficients. The Bayesian learner refines this prior as choices are observed, improving cold start accuracy by 5–40 percentage points across seven datasets. Compared to state‑of‑the‑art methods, Agentic-Priors outperforms AutoElicit on three datasets, surpasses in‑context learning by up to 18 points, and is valued at roughly 6–40 surveyed users in log‑loss terms, making it especially useful for early deployment users.
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