用AI构建食品救助查询系统，解决信息碎片化难题
Retrieval Challenges in Low-Resource Public Service Information: A Case Study on Food Pantry Access
- 通过爬取公开数据构建索引，结合RAG技术实现自然语言查询
- 实测发现系统对模糊查询响应差，知识库不一致导致结果漂移
- 适合关注公共信息公平获取的研究者与社会科技项目开发者
公共服务信息系统常呈碎片化、格式不一且过时状态，形成低资源检索环境，阻碍及时获取关键服务。本文以食物救助站接入这一社会紧迫问题为研究场景，开发了一种基于AI的对话式检索系统，自动抓取并索引公开的救助站数据，采用检索增强生成（RAG）管道支持网页端自然语言查询。通过社区用户提供的真实查询进行试点评估，分析显示系统在检索鲁棒性、处理不明确查询以及基于不一致知识库的准确生成方面存在显著局限。该研究揭示了低资源环境下信息检索的基础挑战，并推动未来在稳健对话式检索方面的研究，以改善对关键公共资源的访问。
原文摘要 · Abstract (English)
Public service information systems are often fragmented, inconsistently formatted, and outdated. These characteristics create low-resource retrieval environments that hinder timely access to critical services. We investigate retrieval challenges in such settings through the domain of food pantry access, a socially urgent problem given persistent food insecurity. We develop an AI-powered conversational retrieval system that scrapes and indexes publicly available pantry data and employs a Retrieval-Augmented Generation (RAG) pipeline to support natural language queries via a web interface. We conduct a pilot evaluation study using community-sourced queries to examine system behavior in realistic scenarios. Our analysis reveals key limitations in retrieval robustness, handling underspecified queries, and grounding over inconsistent knowledge bases. This ongoing work exposes fundamental IR challenges in low-resource environments and motivates future research on robust conversational retrieval to improve access to critical public resources.
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