用检索增强生成提升股票市场信息检索
原标题：Enhancing Stock Market Information Retrieval with Retrieval-Augmented Generation
AI 摘要
该研究构建了一个面向股票市场信息的检索增强生成（RAG）系统，将检索方法与生成能力结合，以提升金融文本生成的准确性和相关性。研究使用 ROUGE-1、ROUGE-2 和 ROUGE-L 指标在 100 条股票市场相关查询上评估系统，结果显示 ROUGE 分数较高，平均响应时间为 1.16 秒。作者认为该系统能够提供准确及时的股市洞察，是金融信息检索的一种有前景的方法。
正文节选
Enhancing Stock Market Information Retrieval with Retrieval-Augmented Generation Abstract Natural Language Processing (NLP) has been widely applied in financial domains, enabling automated analysis and retrieval of stock market information. However, traditional NLP-based systems often struggle with maintaining contextual accuracy and generating relevant responses in dynamic financial environments. To address these challenges, Retrieval-Augmented Generation (RAG) models have emerged as a powerful