Computer Science > Computation and Language
arXiv:2603.09704 (cs)
[Submitted on 10 Mar 2026]
Title:Evaluation of LLMs in retrieving food and nutritional context for RAG systems
View a PDF of the paper titled Evaluation of LLMs in retrieving food and nutritional context for RAG systems, by Maks Po\v{z}arnik Vavken and 3 other authors
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Abstract:In this article, we evaluate four Large Language Models (LLMs) and their effectiveness at retrieving data within a specialized Retrieval-Augmented Generation (RAG) system, using a comprehensive food composition database. Our method is focused on the LLMs ability to translate natural language queries into structured metadata filters, enabling efficient retrieval via a Chroma vector database. By achieving high accuracy in this critical retrieval step, we demonstrate that LLMs can serve as an accessible, high-performance tool, drastically reducing the manual effort and technical expertise previously required for domain experts, such as food compilers and nutritionists, to leverage complex food and nutrition data. However, despite the high performance on easy and moderately complex queries, our analysis of difficult questions reveals that reliable retrieval remains challenging when queries involve non-expressible constraints. These findings demonstrate that LLM-driven metadata filtering excels when constraints can be explicitly expressed, but struggles when queries exceed the representational scope of the metadata format.
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| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2603.09704 [cs.CL] |
| (or arXiv:2603.09704v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09704
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| Related DOI: | https://doi.org/10.1109/BigData66926.2025.11401545
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View a PDF of the paper titled Evaluation of LLMs in retrieving food and nutritional context for RAG systems, by Maks Po\v{z}arnik Vavken and 3 other authors
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