Research on Large Language Model Q&A Method Based on Specific Domain Regulation Documents
Wei Li, Shimin Wang, Jiangtan Yao · 2024
In response to the lack of relevant high-quality Q&A (question and answer) datasets in some specific domain Q&A systems, this paper proposes a large language model Q&A Method based on specific domain regulation documents. The paper adopts natural language processing technology combined with a large language model. Firstly, data processing is performed on regulatory documents to extract text that meets the requirements. Then extracting the information of interest from the text and structuring it, and performing vector transformation to store it in a vector database for building a knowledge base. At the same time, performing vector transformation on user's question, and using cosine similarity to calculate the distance between the problem vector and the vector in the knowledge base. Finally, a random method is proposed to form the problem context, and the Lang Chain tool is used to return the Q&A results, which are then displayed in the front-end. Through system design and engineering application, this paper proposes a large language model Q&A method based on specific domain regulation documents, which can accurately obtain Q&A results.