让大模型做决策时给出结构化解释，提升透明度与可信度。
STRUX: An LLM for Decision-Making with Structured Explanations
- 将长文本提炼为关键事实表，再通过自省判断其重要性
- 在财报电话会议数据上预测股票投资决策，表现优于基线模型
- 适合需要可解释决策的金融、医疗等高风险场景
日常决策数量庞大，理解其背后的逻辑至关重要。本文提出新型大模型决策框架STRUX，通过提供结构化解释增强决策能力，包括与决策相关的有利和不利事实及其强度。STRUX首先将冗长信息浓缩为关键事实表格，再通过一系列自省步骤识别核心事实，并分类为有利或不利因素。最后微调大模型以识别并优先处理这些关键事实，优化决策。在基于财报电话会议转录内容预测股票投资决策的挑战性任务上，STRUX表现超越多个强基线模型。该方法提升了决策透明度，使用户能理解各因素的影响，是实现可解释大模型决策的重要进展。
原文摘要 · Abstract (English)
Countless decisions shape our daily lives, and it is paramount to understand the how and why behind these choices. In this paper, we introduce a new LLM decision-making framework called STRUX, which enhances LLM decision-making by providing structured explanations. These include favorable and adverse facts related to the decision, along with their respective strengths. STRUX begins by distilling lengthy information into a concise table of key facts. It then employs a series of self-reflection steps to determine which of these facts are pivotal, categorizing them as either favorable or adverse in relation to a specific decision. Lastly, we fine-tune an LLM to identify and prioritize these key facts to optimize decision-making. STRUX has been evaluated on the challenging task of forecasting stock investment decisions based on earnings call transcripts and demonstrated superior performance against strong baselines. It enhances decision transparency by allowing users to understand the impact of different factors, representing a meaningful step towards practical decision-making with LLMs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。