Food4All: New Benchmark Tests AI Agents on Food Assistance Referrals
Researchers introduced Food4All, a framework and benchmark for evaluating how well AI language models can help people find food assistance resources by navigating complex eligibility requirements and user interactions. The benchmark includes 686 Indiana food resources and 300 multi-turn evaluation tasks designed to test how models handle realistic challenges like incomplete information and impatient users. The work reveals that while top models achieve 96% accuracy, they still struggle with constraint-sensitive tasks like verifying schedules and eligibility requirements.
Food4All is a new agentic framework and benchmark designed to evaluate how well large language models can assist people in finding appropriate food assistance resources. The system couples a food-specific search tool with 300 multi-turn evaluation tasks grounded in 686 structured Indiana food resources, testing models across single needs, composite cases with access or document constraints, and five types of non-ideal user interactions (unreasonable demands, rambling responses, impatience, incomplete answers, and inconsistent information). Researchers evaluated six LLMs on requirement grounding, resource retrieval, referral correctness, and interaction efficiency. Although the strongest model achieved 96.33% referral accuracy, diagnostic analysis revealed persistent failures in grounding schedules, eligibility requirements, intake procedures, and document constraints, as well as difficulties preserving valid retrieved resources in final recommendations. The trait-level analysis showed that different user behaviors stress different parts of the referral pipeline, providing insights into where conversational agents need improvement for real-world food assistance applications.
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- arXiv cs.CLCenter
Food4All: An Agentic Framework and Benchmark for Food Resource Navigation with Adaptive User Understanding
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