提取增强的中文文本到SQL生成用于对话式文献搜索
Zhenyu Wang1, Mark Xuefang Zhu1, Guo Li1
1School of Information Management, Nanjing University, Nanjing, Jiangsu, China.
PloS one
|October 27, 2025
概括
这项研究引入了一种针对中国文献数据的新型对话式搜索框架,增强语义精度和复杂查询处理. 该系统使用检索增强生成和思想计划策略实现了高精度.
科学领域:
- 信息科学 信息科学 信息科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 当前的文献搜索系统缺乏语义精度,难以处理复杂的查询.
- 需要先进的框架来改善文献元数据检索.
研究的目的:
- 为中国文献学领域开发一种新的对话式搜索框架.
- 为了提高语义精度,并改善处理复杂的查询在文献搜索.
主要方法:
- 开发了BibSQL,这是第一个用于文献元数据的中文文本到SQL数据集.
- 构建了一个双阶段的对话系统,结合语义检索和基于LLM的SQL生成.
- 为监督相似性学习设计了SoftSimMatch,并使用了Program-of-Thoughts (PoT) 提示来生成SQL.
主要成果:
- 检索增强生成 (RAG) 方法实现了高达96.6%的执行精度.
- 在语义对齐和SQL准确性方面,SoftSimMatch增强的RAG在语义对齐和SQL准确性方面表现优于零射击提示和随机样本选择.
- 在低资源环境中,PoT策略和自我校正将准确匹配的准确度从74.8%提高到82.9%.
结论:
- 拟议的框架证明了智能文献查找的强大通用性和实际适用性.
- 语义相似性学习,RAG和PoT提示的集成为未来的Text-to-SQL应用程序建立了一个可扩展的基础.
- 该研究解决了当前系统的局限性,为更复杂的领域特定搜索工具铺平了道路.
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