STEER: Assessing the Economic Rationality of Large Language Models
<|reference_start|>STEER: Assessing the Economic Rationality of Large Language Models: 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.<|reference_end|>
@article{raman2024steer:,
    title={STEER: Assessing the Economic Rationality of Large Language Models},
    author={Narun Raman, Taylor Lundy, Samuel Amouyal, Yoav Levine, Kevin
  Leyton-Brown, Moshe Tennenholtz},
    journal={arXiv preprint arXiv:2402.09552},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.09552},
    primaryClass={cs.CL econ.GN q-fin.EC}
}