DeLLMa: Decision Making Under Uncertainty with Large Language Models

• URL: http://arxiv.org/abs/2402.02392v3
• Date: Fri, 11 Oct 2024 17:43:48 GMT
• Title: DeLLMa: Decision Making Under Uncertainty with Large Language Models
• Authors: Ollie Liu, Deqing Fu, Dani Yogatama, Willie Neiswanger,
• Abstract summary: DeLLMa is a framework designed to enhance decision-making accuracy in uncertain environments. We show 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.
• Score: 31.77731889916652
• License:
• 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.

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