用AI自动生成市场报告，7分钟出6页，成本仅1美元。
MaRGen: Multi-Agent LLM Approach for Self-Directed Market Research and Analysis
- 分角色协作的AI系统，模拟专业顾问做市场分析。
- 生成6页报告仅需7分钟，成本约1美元，质量接近专家水平。
- 可自动优化报告，适合需要快速洞察的商业用户。
我们提出一个自主框架，利用大语言模型（LLM）实现端到端的商业分析与市场报告生成。系统采用研究者、评审员、写作者和检索器四类专用代理，协同完成数据查询、分析、洞察生成、可视化及报告撰写。这些代理通过上下文学习，模仿亚马逊专业顾问的真实汇报材料，复现其分析方法。框架执行多步流程：从数据库查询到报告生成。我们还引入基于LLM的报告质量评估系统，结果与专家评价高度一致。基于评估反馈，系统实施迭代优化机制，通过自动化评审循环提升报告质量。实验表明，自动化评审循环与顾问的非结构化知识均可显著提升报告质量。在验证中，框架可在7分钟内生成详细的6页报告，成本约1美元。本工作为低成本自动生成市场洞察迈出重要一步。
原文摘要 · Abstract (English)
We present an autonomous framework that leverages Large Language Models (LLMs) to automate end-to-end business analysis and market report generation. At its core, the system employs specialized agents - Researcher, Reviewer, Writer, and Retriever - that collaborate to analyze data and produce comprehensive reports. These agents learn from real professional consultants' presentation materials at Amazon through in-context learning to replicate professional analytical methodologies. The framework executes a multi-step process: querying databases, analyzing data, generating insights, creating visualizations, and composing market reports. We also introduce a novel LLM-based evaluation system for assessing report quality, which shows alignment with expert human evaluations. Building on these evaluations, we implement an iterative improvement mechanism that optimizes report quality through automated review cycles. Experimental results show that report quality can be improved by both automated review cycles and consultants' unstructured knowledge. In experimental validation, our framework generates detailed 6-page reports in 7 minutes at a cost of approximately \$1. Our work could be an important step to automatically create affordable market insights.
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