DeLLMa: Decision Making Under Uncertainty with Large Language Models

Ollie Liu, Deqing Fu, Dani Yogatama, Willie Neiswanger

Keywords: large language models, decision theory, decision making under uncertainty

TL;DR: We introduce an inference-time reasoning procedure for reliable decision making under uncertainty with LLMs, drawing upon principles from classical decision theory.

Abstract: The potential of large language models (LLMs) as decision support tools is increasingly being explored in fields such as business, engineering, and medicine, which often face challenging tasks of *decision-making under uncertainty*. In this paper, we show that directly prompting LLMs on these types of decision-making problems can yield poor results, especially as the problem complexity increases. To aid in these tasks, we propose DeLLMa (Decision-making Large Language Model assistant), a framework designed to enhance decision-making accuracy in uncertain environments. DeLLMa involves a multi-step reasoning procedure that integrates recent best practices in scaling *inference-time reasoning*, drawing upon principles from decision theory and utility theory, to provide an accurate and human-auditable decision-making process. We validate our procedure on multiple realistic decision-making environments, demonstrating that DeLLMa can consistently enhance the decision-making performance of leading language models, and achieve up to a 40% increase in accuracy over competing methods. Additionally, we show how performance improves when scaling compute at test time, and carry out human evaluations to benchmark components of DeLLMa.

Primary Area: other topics in machine learning (i.e., none of the above)

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Submission Number: 4817