Author(s)

Xiangcan Li ¹

Affiliation(s)

¹ Bytedance Inc, San Jose, 95131, USA

Corresponding Author

Xiangcan Li

ABSTRACT

In the field of complex query processing, traditional natural language processing models are often difficult to effectively deal with the diversity and complexity of query contents. The Retrieval Augmented Generation (RAG) model demonstrates unique advantages in processing complex queries by combining the two processes of retrieval and generation. This paper provides an in-depth discussion on the working principle of the RAG model and applies it to complex query processing scenarios. By analyzing real cases and validating experimental results, we demonstrate the significant advantages of the RAG model in enhancing query processing results. Although the RAG model shows good performance in processing complex queries, its application still faces some challenges and limitations. This paper concludes with an outlook on the future development of the RAG model, exploring possible optimization directions and application prospects.

KEYWORDS

RAG model, retrieval enhancement generation, complex query processing, natural language processing, deep learning

CITE THIS PAPER

Xiangcan Li, Application of RAG Model Based on Retrieval Enhanced Generation Technique in Complex Query Processing. Advances in Computer, Signals and Systems (2024) Vol. 8: 47-53. DOI: http://dx.doi.org/10.23977/acss.2024.080608.

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