Retrieval Challenges in Low-Resource Public Service Information: A Case Study on Food Pantry Access
This work addresses retrieval challenges in low-resource environments for public service access, such as food pantries, but is incremental as it builds on existing RAG methods to expose limitations in a specific domain.
The paper tackled the problem of fragmented and outdated public service information by developing an AI-powered conversational retrieval system for food pantry access, revealing key limitations in retrieval robustness and handling underspecified queries through a pilot evaluation study.
Public service information systems are often fragmented, inconsistently formatted, and outdated.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.