用多智能体LLM生成财报会议分析报告，提升洞察力。
From Facts to Insights: A Study on the Generation and Evaluation of Analytical Reports for Deciphering Earnings Calls
- 设计多个专业智能体，从不同视角生成报告。
- 多智能体生成报告更深入，但人类专家仍更受青睐。
- 验证了LLM评估报告质量的有效性，与人工评分高度一致。
本文研究大型语言模型（LLMs）在生成和评估财报会议（Earnings Calls, ECs）分析报告中的应用。针对当前研究空白，提出基于多智能体框架的报告生成方法，设计具有多样化视角和分析主题的专用智能体，以丰富报告内容。通过多项分析，考察生成报告与人工撰写报告的一致性，以及个体与集体智能体的影响。结果表明，引入更多智能体可生成更具洞察力的报告，但多数情况下人类专家撰写的报告仍更受欢迎。最后，探讨报告评估难题，在不同场景下分析LLM评估生成报告质量的能力，发现其在多个维度上与人类专家评分存在显著相关性。
原文摘要 · Abstract (English)
This paper explores the use of Large Language Models (LLMs) in the generation and evaluation of analytical reports derived from Earnings Calls (ECs). Addressing a current gap in research, we explore the generation of analytical reports with LLMs in a multi-agent framework, designing specialized agents that introduce diverse viewpoints and desirable topics of analysis into the report generation process. Through multiple analyses, we examine the alignment between generated and human-written reports and the impact of both individual and collective agents. Our findings suggest that the introduction of additional agents results in more insightful reports, although reports generated by human experts remain preferred in the majority of cases. Finally, we address the challenging issue of report evaluation, we examine the limitations and strengths of LLMs in assessing the quality of generated reports in different settings, revealing a significant correlation with human experts across multiple dimensions.
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