Large language models (LLMs) are increasingly used across society, including
in domains like business, engineering, and medicine. These fields often grapple
with decision-making under uncertainty, a critical yet challenging task. In
this paper, we show that directly prompting LLMs on these types of
decision-making problems yields poor results, especially as the problem
complexity increases. To overcome this limitation, 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 scaffolding procedure, drawing upon principles from decision theory
and utility theory, to provide an optimal and human-auditable decision-making
process. We validate our framework on decision-making environments involving
real agriculture and finance data. Our results show that DeLLMa can
significantly improve LLM decision-making performance, achieving up to a 40%
increase in accuracy over competing methods.

大型语言模型（LLM）在商业、工程和医学等领域日益广泛应用。本文提出了 DeLLMa（不确定环境下的决策型大型语言模型助手）框架，通过多步骤的脚手架过程，绘制决策理论和效用理论的原则，提供一种优化且可由人类审查的决策过程，从而显著提高 LLM 的决策性能，使准确率提高了 40%。