Agentic-Priors: Using Large Language Models to Address the Cold Start Problem in Preference Elicitation
To address this, we present Agentic-Priors, which lets agentic systems use the implicit knowledge of large language models (LLMs) instead.
ProofPaper ↗
Key points
- 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.
- This prior is then refined by a Bayesian learner as choices are observed.
- Across seven choice datasets, Agentic-Priors improves cold start accuracy over an uninformed prior by 5-40 percentage points.
Sources (1)
- [1]Agentic-Priors: Using Large Language Models to Address the Cold Start Problem in Preference ElicitationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 02:14 PM
To address this, we present Agentic-Priors, which lets agentic systems use the implicit knowledge of large language models (LLMs) instead.
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.
Extractive summary: sentences quoted from the sources.
Before this
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