GRASP让市民轻松读懂市政预算，准确率超78%。
GRASP: Municipal Budget AI Chatbots for Enhancing Civic Engagement
- 结合检索增强生成与智能代理流程，精准回应预算问题。
- 测试中准确率达78%，显著优于GPT-4o（60%）和Gemini（35%）。
- 适合提升公众参与度，推动政府透明化决策。
随着AI应用增多，针对市民市政预算疑问的专用工具仍属空白。本文提出GRASP框架——基于检索与行动系统生成提示，融合检索增强生成（RAG）、智能体工作流、提示工程及本地财政知识，联合镇级官员确保回答真实性。测试显示，该聊天机器人在78%的情况下提供精确答案，而GPT-4o和Gemini分别仅达60%和35%。GRASP大幅降低公众理解预算所需时间与精力，促进社区讨论，增强政府透明度，助力公民做出更明智决策。
原文摘要 · Abstract (English)
There are a growing number of AI applications, but none tailored specifically to help residents answer their questions about municipal budget, a topic most are interested in but few have a solid comprehension of. In this research paper, we propose GRASP, a custom AI chatbot framework which stands for Generation with Retrieval and Action System for Prompts. GRASP provides more truthful and grounded responses to user budget queries than traditional information retrieval systems like general Large Language Models (LLMs) or web searches. These improvements come from the novel combination of a Retrieval-Augmented Generation (RAG) framework ("Generation with Retrieval") and an agentic workflow ("Action System"), as well as prompt engineering techniques, the incorporation of municipal budget domain knowledge, and collaboration with local town officials to ensure response truthfulness. During testing, we found that our GRASP chatbot provided precise and accurate responses for local municipal budget queries 78% of the time, while GPT-4o and Gemini were only accurate 60% and 35% of the time, respectively. GRASP chatbots greatly reduce the time and effort needed for the general public to get an intuitive and correct understanding of their town's budget, thus fostering greater communal discourse, improving government transparency, and allowing citizens to make more informed decisions.
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