Improving citizen-government interactions with generative artificial intelligence: Novel human-computer interaction strategies for policy understanding through large language models
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a Retrieval-Augmented Generation (RAG) system using Large Language Models (LLMs) to enhance government policy communication, improving citizen understanding and engagement with public administration information.
Area Of Science
- Public Administration
- Artificial Intelligence
- Information Science
Background
- Effective government policy communication faces challenges like complexity and accessibility.
- Digital transformation and Generative AI offer new avenues for public administration.
- Citizen engagement is vital for transparency and democratic processes.
Purpose Of The Study
- To develop and evaluate a system using Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) to improve government policy communication.
- To address challenges in policy accessibility, comprehensibility, and citizen engagement.
- To enhance the dynamic interaction between public authorities and citizens.
Main Methods
- Implementation of a system combining Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs).
- Utilizing a sophisticated retrieval mechanism to generate accurate and comprehensible responses to citizen policy queries.
- Experimentation with a diverse dataset of over 200 policy documents from Chinese and US regional governments.
Main Results
- The system achieved high accuracy rates: 85.58% for Chinese policies and 90.67% for US policies.
- Evaluation metrics included accuracy, comprehensibility, and public engagement, showing significant improvements.
- Case studies demonstrated the system's positive impact on transparency and citizen interaction.
Conclusions
- The integrated RAG and LLM system significantly enhances policy communication accessibility and understandability.
- The approach effectively boosts public engagement, contributing to a more informed and participatory democratic process.
- This innovative method represents a substantial advancement over traditional policy dissemination methods.
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