Retrieval Augmented Fault Diagnosis Knowledge Question Answering Model
Large Language Models(LLMs)have demonstrated exceptional performance in various domains such as everyday conversations,code writing,and emotion recognition.However,LLMs exhibit hallucination problems when performing question-answering tasks in the field of fault diagnosis.To address this issue,we propose a retrieval-enhanced fault diagnosis knowledge question-answering model.This model incorporates a self-constructed knowledge base specific to the fault diagnosis domain,significantly improving the LLMs question-answering capabilities in this field and alleviating the hallucination problems associated with fault diagnosis tasks.In handling fault diagnosis knowledge question-answering and fault type determination tasks,this model greatly outperforms conventional LLMs.
万方数据
维普
