Abstract: Large language models have made significant progress in natural language processing tasks,but their reliance on knowledge encapsulated within parameters can easily lead to the phenomenon of hallucinations.To mitigate this issue,retrieval-augmented generation techniques reduce the risk of errors through information retrieval methods.However,existing methods often retrieve documents that contain inaccurate or misleading information,and there is a lack of discriminative accuracy in evaluating document relevance.In response to these challenges,this study designs a concise and efficient method that combines sparse retrieval with dense retrieval,taking into account both lexical overlap and semantic relevance.Furthermore,a ranker is introduced to reorder the retrieved candidate paragraphs,with the input to the ranker infused with scores from both sparse and dense retri-eval,further optimizing the quality of paragraph ranking.To validate the effectiveness of this method,experiments were conducted on the SQuAD and HotpotQA datasets,and comparisons were made with existing benchmark methods.The experimental results demonstrate that this method holds a significant advantage in enhancing question-answering performance.

Key words: Large language model, Retrieval-augmented generation, Information retrieval