Abstract: Large language models have demonstrated formidable comprehension abilities in conversational tasks,yet they still face issues such as data timeliness and inefficiency in handling specific knowledge.To address these challenges,Retrieval-augmented Generation(RAG) has emerged as an effective solution.However,existing RAG systems still encounter significant challenges,including query understanding bias,inflexible external knowledge retrieval strategies,and low relevance of retrieval results.In response to these issues,this paper proposes a Dynamic Fine-grained Retrieval-augmented Generation(DF-RAG) approach.This method comprises three modules:a query understander,a knowledge selector,and a response generator.By rewriting queries and incorporating externally relevant documents into response generation,it enhances the retrieval-augmented large language model pipeline,achieving dynamic fine-grained retrieval augmentation.Comparative experiments and analyses are conducted on four open-domain question answering datasets against four different types of benchmarks.The results indicate that DF-RAG can more effectively integrate external knowledge with the model's inherent knowledge when handling complex and ambiguous queries.This study holds significant importance for improving the model's text retrieval and response generation capabilities in complex tasks.

Key words: Large language models, Retrieval-augmented generation, Knowledge question answering, Prompt engineering, Information retrieval