Query Rewriting in Retrieval-Augmented Generation as an Application of NLP Technology for Understanding and Transforming Human Language
In the context of the rapid development of large-scale model technology, natural language technology has become a bridge connecting humans and computers, which determines the naturalness and accuracy of human-computer interaction. Therefore, it is very important to accurately convert human language into machine language, and avoid AI hallucinations and unnecessary errors. In this paper, the main exploration, cutting-edge applications and various technologies of retrieval-augmented generation (RAG) technology in the current field are reviewed, covering the whole process from query understanding to retrieval optimization to generation of controllable links. It also summarizes the four directions of RAG technology, namely, complex query deconstruction, query information regulation, end-to-end optimization, fine-grained semantics and vocabulary perception. By combing the key methods and the latest progress in various directions, this paper expounds its important value in improving the reliability of large model answers, reducing factual errors, and enhancing the robustness of the system, and predicts the future development direction of this field, in order to provide reference and inspiration for related research.