FruitQA-Sys:面向农业果蔬领域的专家问答系统OA

A domain-specific expert question-answering system,FruitQA-Sys,was proposed by inte-grating domain data construction,parameter-efficient fine-tuning,and retrieval-augmented generation to solve the problems of insufficient knowledge coverage and difficulty in meeting the practical requirements of intelligent applications in specialized sub-scenarios including the planting of fruit and vegetable with existing large language models.The proposed system constructed a large-scale and high-quality fruit and vegetable question-answer dataset through a generator-scorer module,finely tuned the domain-specific large model FruitBOT based on the LoRA strategy,and incorporated the guided retrieval-augmented generation tech-nique IntroRAG to achieve accurate question and answer of professional knowledge of fruit and vegetable.The results show that in automatic evaluation,the FruitQA-Sys achieves a BERTScore of 0.76,which is a 1.3%improvement over the DeepSeek-V3,and is on par with another mainstream general-purpose large model Qwen-3.0;its GLEU score is 0.37,representing a 2.7%improvement over both DeepSeek-V3 and Qwen-3.0.The overall score in human evaluation reached 8.56,representing improvements of 11.5%and 15.7%over DeepSeek-V3 and Qwen-3.0,respectively.It is indicated that the collaborative mechanism of domain-specific fine-tuning and IntroRAG can effectively improve the professionalism and accuracy of intel-ligent question-answering systems in scenarios of planting fruit and vegetable.