Yun Lixin, Yun Sheng, Xue Haoran
School of Humanities and Social Sciences, Qingdao Agricultural University, Qingdao, Shandong, China.
Computer and Information Science, Fordham University, New York, New York, United States of America.
PLoS One. 2024 Dec 17;19(12):e0311410. doi: 10.1371/journal.pone.0311410. eCollection 2024.
Effective communication of government policies to citizens is crucial for transparency and engagement, yet challenges such as accessibility, complexity, and resource constraints obstruct this process. In the digital transformation and Generative AI era, integrating Generative AI and artificial intelligence technologies into public administration has significantly enhanced government governance, promoting dynamic interaction between public authorities and citizens. This paper proposes a system leveraging the Retrieval-Augmented Generation (RAG) technology combined with Large Language Models (LLMs) to improve policy communication. Addressing challenges of accessibility, complexity, and engagement in traditional dissemination methods, our system uses LLMs and a sophisticated retrieval mechanism to generate accurate, comprehensible responses to citizen queries about policies. This novel integration of RAG and LLMs for policy communication represents a significant advancement over traditional methods, offering unprecedented accuracy and accessibility. We experimented with our system with a diverse dataset of policy documents from both Chinese and US regional governments, comprising over 200 documents across various policy topics. Our system demonstrated high accuracy, averaging 85.58% for Chinese and 90.67% for US policies. Evaluation metrics included accuracy, comprehensibility, and public engagement, measured against expert human responses and baseline comparisons. The system effectively boosted public engagement, with case studies highlighting its impact on transparency and citizen interaction. These results indicate the system's efficacy in making policy information more accessible and understandable, thus enhancing public engagement. This innovative approach aims to build a more informed and participatory democratic process by improving communication between governments and citizens.
向公民有效传达政府政策对于透明度和公民参与至关重要，但诸如可及性、复杂性和资源限制等挑战阻碍了这一进程。在数字转型和生成式人工智能时代，将生成式人工智能和人工智能技术融入公共行政显著提升了政府治理水平，促进了公共当局与公民之间的动态互动。本文提出了一个利用检索增强生成（RAG）技术与大语言模型（LLM）相结合的系统，以改善政策沟通。针对传统传播方式中存在的可及性、复杂性和参与度等挑战，我们的系统使用大语言模型和精密的检索机制，对公民有关政策的询问生成准确、易懂的回复。这种将RAG和大语言模型用于政策沟通的新颖整合代表了相对于传统方法的重大进步，提供了前所未有的准确性和可及性。我们用来自中国和美国地方政府的多样化政策文件数据集对我们的系统进行了试验，该数据集包含200多份涉及各种政策主题的文件。我们的系统显示出很高的准确性，中国政策平均准确率为85.58%，美国政策平均准确率为90.67%。评估指标包括准确性、可理解性和公众参与度，与专家人工回复和基线比较进行衡量。该系统有效地提高了公众参与度，案例研究突出了其对透明度和公民互动的影响。这些结果表明该系统在使政策信息更易获取和理解从而增强公众参与方面的有效性。这种创新方法旨在通过改善政府与公民之间的沟通来构建一个更具信息性和参与性的民主进程。
