金融领域RAG系统优化，提升财报分析准确率
Multi-Reranker: Maximizing performance of retrieval-augmented generation in the FinanceRAG challenge
- 通过查询扩展与语料精炼提升检索前效果
- 多重重排序模型显著提高检索精度
- 创新长文本处理方法，兼顾生成质量与效率
随着大语言模型在特定领域应用的拓展，其在金融领域的使用迅速增长。本论文介绍了针对ACM-ICAIF '24 FinanceRAG挑战赛所开发的高性能、金融专用检索增强生成（RAG）系统。通过在预检索阶段对查询扩展和语料精炼进行消融实验，优化系统性能；为提升检索准确率，采用多个重排序模型；并提出一种高效的长上下文管理方法，在生成阶段显著改善响应质量而不损失性能。最终在FinanceRAG挑战赛中取得第二名。主要贡献包括：（1）预检索阶段的消融分析，（2）增强的检索算法，（3）新颖的长上下文处理策略。该工作展示了大模型在复杂金融数据处理与精准洞察生成方面的潜力。源代码及更多细节见https://github.com/cv-lee/FinanceRAG。
原文摘要 · Abstract (English)
As Large Language Models (LLMs) increasingly address domain-specific problems, their application in the financial sector has expanded rapidly. Tasks that are both highly valuable and time-consuming, such as analyzing financial statements, disclosures, and related documents, are now being effectively tackled using LLMs. This paper details the development of a high-performance, finance-specific Retrieval-Augmented Generation (RAG) system for the ACM-ICAIF '24 FinanceRAG competition. We optimized performance through ablation studies on query expansion and corpus refinement during the pre-retrieval phase. To enhance retrieval accuracy, we employed multiple reranker models. Notably, we introduced an efficient method for managing long context sizes during the generation phase, significantly improving response quality without sacrificing performance. We ultimately achieve 2nd place in the FinanceRAG Challenge. Our key contributions include: (1) pre-retrieval ablation analysis, (2) an enhanced retrieval algorithm, and (3) a novel approach for long-context management. This work demonstrates the potential of LLMs in effectively processing and analyzing complex financial data to generate accurate and valuable insights. The source code and further details are available at https://github.com/cv-lee/FinanceRAG.
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