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

• URL: http://arxiv.org/abs/2602.21598v1
• Date: Wed, 25 Feb 2026 05:48:15 GMT
• Title: Retrieval Challenges in Low-Resource Public Service Information: A Case Study on Food Pantry Access
• Authors: Touseef Hasan, Laila Cure, Souvika Sarkar,
• Abstract summary: We develop an AI-powered conversational retrieval system that scrapes and indexes publicly available pantry data.<n>We conduct a pilot evaluation study using community-sourced queries to examine system behavior in realistic scenarios.
• Score: 0.4773403254712565
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Public service information systems are often fragmented, inconsistently formatted, and outdated.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.

Related papers

• How good are LLMs at Retrieving Documents in a Specific Domain? [3.282961543904818]
  We propose an automated method to curate a domain-specific evaluation dataset to analyze the capability of a search system.<n>We incorporate the Retrieval of Augmented Generation (RAG), powered by Large Language Models (LLMs), for high-quality retrieval of environmental domain data using natural language queries.
  arXiv  Detail & Related papers  (2025-08-25T19:47:21Z)
• Meet Your New Client: Writing Reports for AI -- Benchmarking Information Loss in Market Research Deliverables [0.0]
  This study evaluates information loss during their ingestion into RAG systems.<n>Findings show that while text is reliably extracted, significant information is lost from complex objects like charts and diagrams.<n>This suggests a need for specialized, AI-native deliverables to ensure research insights are not lost in translation.
  arXiv  Detail & Related papers  (2025-08-17T12:05:44Z)
• SpiritRAG: A Q&A System for Religion and Spirituality in the United Nations Archive [4.575515160275914]
  We present SpiritRAG, an interactive Question Answering (Q&A) system based on Retrieval-Augmented Generation (RAG)<n>Built using 7,500 United Nations (UN) resolution documents related to R/S in the domains of health and education, SpiritRAG allows researchers and policymakers to conduct complex, context-sensitive database searches.<n>A pilot test and evaluation with domain experts on 100 manually composed questions demonstrates the practical value and usefulness of SpiritRAG.
  arXiv  Detail & Related papers  (2025-07-06T13:54:54Z)
• From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents [96.65646344634524]
  Large Language Models (LLMs), endowed with reasoning and agentic capabilities, are ushering in a new paradigm termed Agentic Deep Research.<n>We trace the evolution from static web search to interactive, agent-based systems that plan, explore, and learn.<n>We demonstrate that Agentic Deep Research not only significantly outperforms existing approaches, but is also poised to become the dominant paradigm for future information seeking.
  arXiv  Detail & Related papers  (2025-06-23T17:27:19Z)
• A GenAI System for Improved FAIR Independent Biological Database Integration [0.0]
  We introduce an experimental natural language-based query processing system designed to empower scientists to discover, access, and query biological databases.<n> FAIRBridge harnesses the capabilities of AI to interpret query intents, map them to relevant databases, and generate executable queries.<n>The system also includes robust tools for mitigating low-quality query processing, ensuring high fidelity and responsiveness in the information delivered.
  arXiv  Detail & Related papers  (2025-06-22T08:04:24Z)
• InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation [63.55258191625131]
  InfoDeepSeek is a new benchmark for assessing agentic information seeking in real-world, dynamic web environments.<n>We propose a systematic methodology for constructing challenging queries satisfying the criteria of determinacy, difficulty, and diversity.<n>We develop the first evaluation framework tailored to dynamic agentic information seeking, including fine-grained metrics about the accuracy, utility, and compactness of information seeking outcomes.
  arXiv  Detail & Related papers  (2025-05-21T14:44:40Z)
• Information Retrieval Induced Safety Degradation in AI Agents [52.15553901577888]
  This study investigates how expanding retrieval access affects model reliability, bias propagation, and harmful content generation.<n>Retrieval-enabled agents built on aligned LLMs often behave more unsafely than uncensored models without retrieval.<n>These findings underscore the need for robust mitigation strategies to ensure fairness and reliability in retrieval-enabled and increasingly autonomous AI systems.
  arXiv  Detail & Related papers  (2025-05-20T11:21:40Z)
• AI Enhanced Ontology Driven NLP for Intelligent Cloud Resource Query Processing Using Knowledge Graphs [0.0]
  This paper proposes an advanced Natural Language Processing (NLP) enhanced by ontology-based semantics to enable intuitive, human-readable queries.<n>The proposed framework enables dynamic intent extraction and relevance ranking using Latent Semantic Indexing (LSI) and AI models.
  arXiv  Detail & Related papers  (2025-02-10T02:15:13Z)
• Object Detectors in the Open Environment: Challenges, Solutions, and Outlook [95.3317059617271]
  The dynamic and intricate nature of the open environment poses novel and formidable challenges to object detectors. This paper aims to conduct a comprehensive review and analysis of object detectors in open environments. We propose a framework that includes four quadrants (i.e., out-of-domain, out-of-category, robust learning, and incremental learning) based on the dimensions of the data / target changes.
  arXiv  Detail & Related papers  (2024-03-24T19:32:39Z)
• DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text [73.68051228972024]
  Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge. Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
  arXiv  Detail & Related papers  (2023-10-31T04:37:57Z)
• On the Social and Technical Challenges of Web Search Autosuggestion Moderation [118.47867428272878]
  Autosuggestions are typically generated by machine learning (ML) systems trained on a corpus of search logs and document representations. While current search engines have become increasingly proficient at suppressing such problematic suggestions, there are still persistent issues that remain. We discuss several dimensions of problematic suggestions, difficult issues along the pipeline, and why our discussion applies to the increasing number of applications beyond web search.
  arXiv  Detail & Related papers  (2020-07-09T19:22:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.