多模态数据驱动的Al智能体模式设计
Design of AI Agent Models Driven by Multimodal Data
韩雪雯 1车尚锟 1杨梦晴 2王能3
作者信息
- 1. 清华大学经济管理学院 北京 100084
- 2. 南京师范大学新闻与传播学院 南京 210023
- 3. 加州大学洛杉矶分校 洛杉矶90095
- 折叠
摘要
[目的/意义]针对不同任务的性质设计用于多模态数据处理的AI智能体模式,有利于深入挖掘多模态数据价值,从而支持决策过程.[方法/过程]设计一种结合水平智能体结构与垂直智能体结构的混合模式,以智能投资场景中财务年度报告为例,选择美股市场道琼斯指数的30支股票,对各个公司2023年度年报进行智能投资研究分析.利用大型语言模型处理多模态数据的能力,分析公司基本面、市场情绪与风险和机会3个子任务,并根据子任务做出最终的投资决策.[结果/结论]垂直结构在要求工作效率和清晰决策的分析中表现出色.水平结构在需要协作、灵活性和广泛汇集知识与专业技能的场景中表现更佳.针对不同的任务选择最佳的混合智能体结构后,在最终的投资决策准确性上比单一模式的智能体结构表现更好.
Abstract
[Purpose/Significance]This study develops an AI agent model for multimodal data processing across varied tasks.By exploring the inherent value of multimodal data,the model enhances the support for de-cision-making processes.[Method/Process]This study designed a hybrid AI agent model that integrated both horizontal and vertical structures.Using the financial annual reports from thirty Dow Jones Industrial Average companies as a case study,this model facilitated intelligent investment research and analysis for the year 2023.The research compared the performance of different organizational structures of financial agents in executing three sub-tasks:analysis of company fundamentals,market sentiment,and risk and opportunity assessment.Decisions were made based on these analyses,leveraging large language models capable of processing multimodal data.[Result/Conclusion]The vertical agent structure excels in tasks requiring efficient,directional strategic analysis with clear hierarchical decision-making.Conversely,the horizontal structure is more effective in scenarios demanding collabo-ration,flexibility,and broad expertise.The hybrid model outperforms single-structure agents in terms of accuracy in the final investment decision,demonstrating its efficacy in optimizing task-specific agent configurations.
关键词
多模态数据/AI智能体/水平智能体结构/垂直智能体结构/金融决策Key words
multi-modal data/AI agents/horizontal agent structure/vertical agent structure/financial de-cision引用本文复制引用
出版年
2024
NETL
NSTL
万方数据