STEER: Assessing the Economic Rationality of Large Language Models

• URL: http://arxiv.org/abs/2402.09552v2
• Date: Tue, 28 May 2024 16:27:56 GMT
• Title: STEER: Assessing the Economic Rationality of Large Language Models
• Authors: Narun Raman, Taylor Lundy, Samuel Amouyal, Yoav Levine, Kevin Leyton-Brown, Moshe Tennenholtz,
• Abstract summary: There is increasing interest in using LLMs as decision-making "agents" determining whether an LLM agent is reliable enough to be trusted requires a methodology for assessing such an agent's economic rationality.
• Score: 21.91812661475551
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There is increasing interest in using LLMs as decision-making "agents." Doing so includes many degrees of freedom: which model should be used; how should it be prompted; should it be asked to introspect, conduct chain-of-thought reasoning, etc? Settling these questions -- and more broadly, determining whether an LLM agent is reliable enough to be trusted -- requires a methodology for assessing such an agent's economic rationality. In this paper, we provide one. We begin by surveying the economic literature on rational decision making, taxonomizing a large set of fine-grained "elements" that an agent should exhibit, along with dependencies between them. We then propose a benchmark distribution that quantitatively scores an LLMs performance on these elements and, combined with a user-provided rubric, produces a "STEER report card." Finally, we describe the results of a large-scale empirical experiment with 14 different LLMs, characterizing the both current state of the art and the impact of different model sizes on models' ability to exhibit rational behavior.

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