Can an LLM Learn Preferences from Choice Data?

Jeongbin Kim, Matthew Kovach, Kyu-Min Lee, Euncheol Shin and Hector Tzavellas

Papers from arXiv.org

Abstract: Can large language models (LLMs) learn a decision maker's preferences from observed choices and generate preference-consistent recommendations in new situations? We propose a portable Simulate-Recommend-Evaluate framework that tests preference learning from revealed-choice data by comparing LLM recommendations with optimal choices implied by known preference primitives. We apply the framework to choice under uncertainty using the disappointment aversion model. Recommendation accuracy improves as models observe more choices, but learning is heterogeneous across preference types and LLMs: GPT learns risk aversion better than disappointment aversion, Gemini performs best in high disappointment-aversion regions, and Claude shows the broadest effective learning across parameter regions.

Date: 2024-01, Revised 2026-04
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp, nep-dcm and nep-upt
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