Can an LLM Learn Preferences from Choice Data?
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915918129397760 |
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| author | Kim, Jeongbin Kovach, Matthew Lee, Kyu-Min Shin, Euncheol Tzavellas, Hector |
| author_facet | Kim, Jeongbin Kovach, Matthew Lee, Kyu-Min Shin, Euncheol Tzavellas, Hector |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_07345 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Can an LLM Learn Preferences from Choice Data? Kim, Jeongbin Kovach, Matthew Lee, Kyu-Min Shin, Euncheol Tzavellas, Hector General Economics Economics 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. |
| title | Can an LLM Learn Preferences from Choice Data? |
| topic | General Economics Economics |
| url | https://arxiv.org/abs/2401.07345 |