食品科学2025，Vol.46Issue(22)：13-22,10.DOI:10.7506/spkx1002-6630-20250408-059
基于增强检索生成框架的食品安全监管智能问答系统
An Intelligent Question Answering System for Food Safety Regulation Based on Retrieval-Augmented Generation Framework
摘要
Abstract
The food safety regulation question answering(QA)task imposes high requirements on model accuracy,compliance,and interpretability.However,existing large language models(LLMs)face challenges in this domain,including imprecise knowledge retrieval,insufficient regulatory interpretation capabilities,and high computational costs.To address these issues,we proposed an intelligent question answering system based on the retrieval augmented generation(RAG)framework,with its core being the food safety regulation large language model(FSR-LLM).By optimizing database storage structures,retrieval strategies and the generator,FSR-LLM enhanced the quality and efficiency of food safety regulation QA.First,we constructed a food safety knowledge graph(KG)database to store regulatory provisions,food safety standards,and related data in a structured manner,improving the model's capability to organize and utilize domain-specific knowledge.Additionally,we introduced an LLM-guided retrieval strategy,which enables intelligent query parsing and accurately extracts highly relevant information from the food safety regulation KG,reducing the retrieval of irrelevant or misleading contents.For the generator module,we fine-tuned Qwen-7B-Chat using low-rank adaptation(LoRA),ensuring better alignment with food safety QA tasks,while significantly reducing computational costs,allowing training on a single RTX 4090 GPU.Experimental results on the proposed dataset demonstrated that FSR-LLM outperformed baseline models in BLEU-4,Rouge-L,and accuracy,exhibiting higher precision and semantic coherence.This work provides a low-cost,high-performance,and scalable solution for intelligent food safety regulation.关键词
食品安全监管/检索增强生成/知识图谱/低秩适应/微调Key words
food safety regulation/retrieval-augmented generation/knowledge graph/low-rank adaptation/fine-tuning分类
轻工业引用本文复制引用
毛典辉,王可浩,陈俊华,徐静婷..基于增强检索生成框架的食品安全监管智能问答系统[J].食品科学,2025,46(22):13-22,10.基金项目
北京市自然科学基金项目(9232005) （9232005）
