Public service information systems are often fragmented, inconsistently formatted, and outdated.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.

翻译：公共服务信息系统通常呈现碎片化、格式不一致且信息陈旧的特点。这些特性形成了低资源检索环境，阻碍了关键服务的及时获取。本研究以食品储藏室访问这一社会紧迫问题为切入点，探讨此类环境中的检索挑战。我们开发了一个基于人工智能的对话式检索系统，该系统通过抓取和索引公开可用的储藏室数据，并采用检索增强生成（RAG）流程，以支持通过网页界面进行自然语言查询。我们利用社区来源的查询开展试点评估研究，以检验系统在真实场景中的表现。分析揭示了检索鲁棒性、处理欠明确查询以及在知识库不一致情况下进行事实锚定等方面存在的主要局限。这项持续进行的工作揭示了低资源环境下的基础信息检索挑战，并为未来开展鲁棒对话式检索研究以改善关键公共资源获取提供了研究动机。