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Retrieval Challenges in Low-Resource Public Service Information: A Case Study on Food Pantry Access

Touseef Hasan, Laila Cure, Souvika Sarkar

TL;DR

An AI-powered conversational retrieval system that scrapes and indexes publicly available pantry data and employs a Retrieval-Augmented Generation pipeline to support natural language queries via a web interface is developed.

Abstract

Public service information systems are often fragmented, inconsistently formatted, and outdated.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

Retrieval Challenges in Low-Resource Public Service Information: A Case Study on Food Pantry Access

TL;DR

An AI-powered conversational retrieval system that scrapes and indexes publicly available pantry data and employs a Retrieval-Augmented Generation pipeline to support natural language queries via a web interface is developed.

Abstract

Public service information systems are often fragmented, inconsistently formatted, and outdated.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.
Paper Structure (4 sections, 1 figure, 1 table)

This paper contains 4 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction and Motivation
  2. Description of the Proposed Solution
  3. Preliminary Results and Challenges
  4. Future Directions and Conclusion

Figures (1)

  • Figure 1: Our proposed Pantry Assistant. Food pantries are scraped from Kansas Food Source website and stored into a structured JSON database. Data is then embedded and indexed for similarity search to enable semantic retrieval. The retrieved relevant pantries are then passed to an LLM, which recommends pantries to users based on their requirements.