用大模型提升营养数据检索效率，但复杂查询仍受限。
Evaluation of LLMs in retrieving food and nutritional context for RAG systems
- 让大模型将自然语言转为结构化筛选条件，驱动向量库高效检索。
- 简单到中等复杂度查询准确率高，难查询召回率显著下降。
- 适合需快速检索营养数据的专家，不适用于表达模糊的复杂问题。
本文评估了四种大语言模型（LLMs）在专用检索增强生成（RAG）系统中，从食品成分数据库中检索信息的能力。方法聚焦于模型将自然语言查询转化为结构化元数据过滤器的能力，从而通过Chroma向量数据库实现高效检索。在关键检索环节取得高准确率，证明大模型可作为易用且高性能的工具，大幅降低食品编纂员和营养师等领域专家使用复杂营养数据所需的手动工作量和技术门槛。然而，尽管在简单和中等复杂度查询上表现良好，对困难问题的分析表明，当查询包含无法表达的约束时，可靠检索仍具挑战性。研究显示，基于大模型的元数据过滤在约束可明确表达时表现优异，但在超出元数据格式表达能力的查询场景下表现不佳。
原文摘要 · Abstract (English)
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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