实施大型语言模型和检索增强生成以提取非法跨国贸易的地理位置
Zifu Wang1, Meng-Hao Li2, Patrick Baxter2
1Department of Geography and Geoinformation Science, NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, USA. zwang31@gmu.edu.
International journal of health geographics
|April 28, 2025
概括
这项研究开发了一个新的数据库,用于绘制全球非法脏贸易网络的地图,确定国家在器官贩运中的角色. 先进的AI模型精确地提取了地理数据,揭示了20多年来动态的贸易模式.
科学领域:
- 人工智能的人工智能
- 全球健康 全球健康
- 犯罪学 犯罪学
背景情况:
- 非法脏贸易涉及复杂的全球网络的买家,卖家,经纪人和外科医生.
- 由于缺乏系统数据和地理信息提取传统方法的局限性,了解这些网络具有挑战性.
- 目前的命名实体识别 (NER) 工具往往无法识别特定国家在器官贩运中的角色.
研究的目的:
- 开发一个全面的跨国贸易国家网络的地理空间数据库.
- 克服在非法器官贸易出版物中提取地理位置和相关角色的现有方法的局限性.
- 分析过去二十年中交易网络的动态演变.
主要方法:
- 利用了来自变压器 (BERT) 和生成预训练变压器 (GPT) 的双向编码器表示模型Llama3.3用于数据库开发.
- 从LexisNexis (2000-2022) 提取了关于非法脏贸易的新闻文章.
- 应用BERT和Llama3.3与思维链提示调整来识别文章相关性和国家角色 (卖家,买家,外科医生,经纪人).
主要成果:
- 伯特模型在文章相关性分类中实现了88.75%的准确性.
- 拉玛3.3模型在角色提取方面表现出高准确度:卖方为86.30%,买方为88.89%,经纪人为93.33%,手术为95.93%.
- 鉴定了随着时间的推移,脏贸易网络中国家角色的动态变化,在十年内,排名最高的国家发生了重大变化.
- 观察到发展中国家主要是收容脏卖家,而发达国家则是收容脏买家.
结论:
- 跨国脏贸易网络的新型地理空间数据库已经成功开发,跨越了20年的时间.
- 与传统的NER和机器学习技术相比,采用人工智能驱动的方法提供了更精确的地理位置和角色提取方法.
- 这些发现强调了全球非法脏贸易的不断变化的性质,以及发展中国家和发达国家所扮演的不同角色.
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