STRUX: An LLM for Decision-Making with Structured Explanations
This addresses the need for transparent decision-making in AI, particularly for users in domains like finance, though it appears incremental as it builds on existing LLM methods.
The paper tackles the problem of enhancing LLM decision-making by introducing STRUX, a framework that provides structured explanations with favorable and adverse facts, and it demonstrated superior performance in forecasting stock investment decisions based on earnings call transcripts.
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.