## Enhancing Citizen-Government Interaction with Generative Artificial Intelligence: Novel Human-Computer Interaction Strategies for Policy Understanding through Large Language Models### Overview of Current Communication StrategiesGovernments around the world utilize a combination of public announcements, official documents, websites, and public forums to disseminate policy information. However, these methods often fail to engage citizens effectively due to factors like complexity, accessibility, and a general lack of public awareness and understanding of governmental procedures.### Financial and Logistical ConstraintsCommunicating policies incurs significant costs, which may limit the government's ability to reach all citizens effectively.### Inherent Challenges in Policy CommunicationPolicy documents tend to be complex and bureaucratic, making them challenging for citizens to understand. Additionally, slow response times to citizen inquiries due to high query volumes and bureaucratic processes can also erode trust in government institutions.### Our Approach to Enhancing Government-Citizen CommunicationWe propose a system that combines Retrieval-Augmented Generation (RAG) technology with Langchain, a tool that dynamically updates the knowledge base with the latest policies and regulations. This approach leverages the capabilities of large language models (LLMs) and combines them with a sophisticated retrieval mechanism to deliver accurate, up-to-date, and comprehensible information on government policies directly to the public.### Implementation in Citizen CommunicationTo maximize the impact of our approach, we propose integrating it within existing government digital platforms, such as official websites, mobile apps chat-bots, and social media channels. This will ensure broad access to the system.### Building the Knowledge Base for RAGThe foundation of our RAG system is its knowledge base, which must be comprehensive, authoritative, and up-to-date. We will compile a wide array of government documents, policies, regulations, and related materials. Additionally, our knowledge base will be incorporated with a process that can have continuous updates, with a mechanism in place for the timely incorporation of new policies, amendments, and relevant announcements into the knowledge base.### Potential Improvement on Government-Citizen CommunicationOur approach significantly enhances the way government policies are communicated to citizens. By utilizing the RAG system integrated with Langchain, we can transform the traditionally complex and opaque process of policy dissemination into a user-friendly, engaging, and interactive experience. This system not only makes policy information more accessible but also more understandable, empowering citizens to gain insights into government actions that directly affect their lives.### Details in the RAG Component in Our ApproachAt the core of our system lies Retrieval-Augmented Generation (RAG) utilizing Langchain technology. RAG harnesses the vast knowledge embedded within LLMs while integrating a retrieval mechanism that pulls in specific, relevant information from a designated knowledge base. This integration ensures that the responses generated are not only linguistically coherent but also deeply grounded in the most current and accurate data available.### Scalability and SecurityOur architecture is designed to facilitate dynamic updating and scaling, allowing for the inclusion of new information and policies as they become available. We ensure seamless integration and synchronization between the retrieval knowledge base and the generative models, enabling the system to adapt to new data and evolving policy landscapes efficiently.### Experiments and ResultsIn comprehensive experiments, we evaluated the effectiveness of our proposed system using a dataset comprising 200 policy documents from both the US and China. Our system demonstrated high accuracy, averaging 85.58% for Chinese and 90.67% for US policies. The system also showed high accuracy in processing quantitative information.### Application ScenariosPotential applications of our system include:- **Policy rollout:** Providing citizens with real-time information about new policies and guidelines.- **Crisis communication:** During emergencies, the system can disseminate critical updates and guidance.- **Complex policy explanation:** Breaking down complex policies into digestible parts for easier understanding.### Discussion and LimitationsWhile our system shows promise, it does have limitations. The accuracy rates varied between US and Chinese policy documents, highlighting linguistic and structural complexities. The case studies revealed challenges with document structure and specific queries. These limitations suggest a need for more refined natural language processing tools and standardized policy documentation practices.### ConclusionOur study demonstrates the potential of integrating RAG technology with LLMs to significantly enhance the communication of government policies to citizens. Through rigorous experimentation and analysis, our system showcased its ability to accurately interpret and respond to a wide range of policy-related queries across different regions and languages. Despite facing challenges with document structure and specificity in queries, our approach highlights a promising direction for public administration, offering a more accessible, transparent, and responsive way for governments to engage with their citizens.