标题： FinTeam：用于全面金融场景的多智能体协作智能系统 显示英文标题

标题： FinTeam: A Multi-Agent Collaborative Intelligence System for Comprehensive Financial Scenarios

摘要： 财务报告生成任务从宏观到微观经济分析范围广泛，还需要进行大量数据分析。 现有的大型语言模型通常是在简单的问答任务上进行微调，无法全面分析真实的财务场景。 鉴于复杂性，财务公司通常将任务分配给不同部门。 受此启发，我们提出了FinTeam，一个财务多智能体协作系统，其工作流程包含四个大型语言模型代理：文档分析器、分析师、会计师和顾问。 我们使用构建的数据集对这些代理进行特定财务专业知识的训练。 我们评估了FinTeam在从真实在线投资论坛构建的综合财务任务上的表现，包括宏观经济、行业和公司分析。 人类评估显示，通过结合代理，从FinTeam生成的财务报告达到了62.00%的接受率，优于基线模型如GPT-4o和玄元。 此外，FinTeam的代理在FinCUGE上的平均改进率为7.43%，在FinEval上的准确率提升了2.06%。 项目可在 https://github.com/FudanDISC/DISC-FinLLM/ 获取。 显示英文摘要

摘要： Financial report generation tasks range from macro- to micro-economics analysis, also requiring extensive data analysis. Existing LLM models are usually fine-tuned on simple QA tasks and cannot comprehensively analyze real financial scenarios. Given the complexity, financial companies often distribute tasks among departments. Inspired by this, we propose FinTeam, a financial multi-agent collaborative system, with a workflow with four LLM agents: document analyzer, analyst, accountant, and consultant. We train these agents with specific financial expertise using constructed datasets. We evaluate FinTeam on comprehensive financial tasks constructed from real online investment forums, including macroeconomic, industry, and company analysis. The human evaluation shows that by combining agents, the financial reports generate from FinTeam achieved a 62.00% acceptance rate, outperforming baseline models like GPT-4o and Xuanyuan. Additionally, FinTeam's agents demonstrate a 7.43% average improvement on FinCUGE and a 2.06% accuracy boost on FinEval. Project is available at https://github.com/FudanDISC/DISC-FinLLM/.