改进公民与政府的交互与生成人工智能:通过大型语言模型,为政策理解提供新的人机交互策略
Lixin Yun1, Sheng Yun2, Haoran Xue2
1School of Humanities and Social Sciences, Qingdao Agricultural University, Qingdao, Shandong, China.
PloS one
|December 17, 2024
概括
本研究引入了使用大型语言模型 (LLM) 的检索增强生成 (RAG) 系统,以增强政府政策沟通,改善公民对公共管理信息的理解和参与.
科学领域:
- 公共管理 公共管理
- 人工智能的人工智能
- 信息科学 信息科学 信息科学
背景情况:
- 有效的政府政策沟通面临着复杂性和可访问性等挑战.
- 数字化转型和生成人工智能为公共管理提供了新的途径.
- 公民参与对于透明度和民主进程至关重要.
研究的目的:
- 开发和评估一个使用检索增强生成 (RAG) 和大型语言模型 (LLM) 的系统,以改善政府政策沟通.
- 解决政策可访问性,可理解性和公民参与方面的挑战.
- 加强公共当局与公民之间的动态互动.
主要方法:
- 实现一个将检索增强生成 (RAG) 与大型语言模型 (LLM) 结合在一起的系统.
- 利用复杂的检索机制,为公民的政策查询提供准确和可理解的答案.
- 对来自中国和美国地区政府的200多份政策文件的多样化数据集进行了实验.
主要成果:
- 该系统实现了高准确率:中国政策为85.58%,美国政策为90.67%.
- 评估指标包括准确性,可理解性和公众参与度,显示了显著的改进.
- 案例研究表明,该系统对透明度和公民互动产生积极影响.
结论:
- 集成的RAG和LLM系统显著提高了政策沟通的可访问性和可理解性.
- 这种方法有效地促进了公众的参与,为更知情和参与的民主进程做出了贡献.
- 这种创新方法与传统的政策传播方法相比,是一个巨大的进步.
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