Agentic-Priors: Using Large Language Models to Address the Cold Start Problem in Preference Elicitation
An agent that learns a user's preferences from their choices faces a cold start problem: before any choices are observed, it must rely solely on a prior. Informative priors are usually fitted to historical choice data, which new tasks and deployments lack. To address this, we present Agentic-Priors, which lets agentic systems use the implicit knowledge of large language models (LLMs) instead. Specifically, an LLM states a new user's trade-offs in interpretable terms, such as willingness to pay or value of time, which we map to a prior over multinomial logit coefficients. This prior is then refined by a Bayesian learner as choices are observed. Across seven choice datasets, Agentic-Priors ...
| Authors | Bruno Arcanjo, Zhaoxing Li, Sebastian Stein |
|---|---|
| Primary category | cs.MA |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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