基于多智能体大语言模型的多语言气候-健康文献自动分析框架

中文摘要 AI 辅助

跨学科和多语言科学文献的快速增长，使得传统人工分析及单算法方法面临效率低、可扩展性差、领域适应性不足的问题。针对典型跨学科气候-健康领域的文献分析需求，本研究提出一种面向多语言科学文献的多智能体大语言模型自动分析框架，实现涵盖文献筛选、结构化信息提取及标准化整合的全流程自动化。该框架以中央协调模块为核心，部署文档评估、信息提取、分析审查三类专用智能体，以模拟领域专家的文献分析思路；同时采用四层幻觉控制策略结合人工验证流程，确保分析结果的准确性与可靠性。在包含1993至2023年中国相关内容的32642篇中英文双语气候-健康论文语料上验证，该框架在核心信息提取任务中F1值达0.92，完成2012对城市-文献关联对的提取与标准化，为气候-健康研究领域的大规模证据挖掘提供有效技术支撑。

英文摘要

The rapid proliferation of interdisciplinary and multilingual scientific literature has left traditional manual analysis and single-algorithm methods plagued by low efficiency, poor scalability, and insufficient domain adaptability. Targeting the literature analysis needs of the typical interdisciplinary climate-health field, this study proposes a multi-agent large language model automated analysis framework for multilingual scientific literature, which realizes full-process automation covering literature screening, structured information extraction, and standardized integration. With a central coordination module as the core, the framework deploys three dedicated agents for document evaluation, information extraction, and analytical review to mimic the literature analysis thinking of domain experts, and adopts a four-layer hallucination control strategy together with a manual verification procedure to ensure the accuracy and reliability of analytical outcomes. Validated on a bilingual Chinese-English corpus of 32,642 climate-health papers covering China from 1993 to 2023, the framework achieves an F1 score of 0.92 in core information extraction, and completes the extraction and standardization of 2,012 city-literature association pairs, offering effective technical support for large-scale evidence mining in the climate-health research domain.

发表机构

• Tsinghua University（清华大学）
• Huawei Technologies Co., Ltd（华为技术有限公司）
• Renmin University of China（中国人民大学）
• Beijing Forestry University（北京林业大学）
• National Institute of Natural Hazards, Ministry of Emergency Management of China（中国应急管理部国家自然灾害防治研究院）

机构由 AI 辅助整理，请以论文原文为准。