Applying embedding models and advanced RAG techniques for Korean text classification

Retrieval-Augmented Generation (RAG) is a method of generating text based on retrieved information, which seeks to overcome the limitations of large language models' pre-trained knowledge by retrieving external knowledge and utilizing it in responses.In RAG-based systems, retrieval performance closely affects the overall quality of responses, so various enhancement techniques to improve retrieval accuracy have emerged as an important challenge.If more precise document retrieval is possible for user queries, it is expected that accurate and reliable answers can be generated.In this paper, we propose a method to compare and analyze retrieval performance and classification performance in four domains by combining various embedding models and advanced RAG techniques to improve retrieval accuracy.To this end, we apply multiple embedding models to Korean text data collected by each domain, and quantitatively analyze the changes in retrieval performance and classification performance using various retrieval enhancement techniques such as HyDE, Multi Query, and Reranker.Finally, the performance evaluation results show that more accurate retrieval and higher classification performance can be achieved by applying advanced RAG techniques than simple embedding-based retrieval.