Dense Retrieval Systems with LLM-Based Query Expansion
Zhizhang Wang, Quanli Pei · 2024
This paper presents a new approach to improving retrieval system performance by integrating Pseudo-Relevance Feedback (PRF) with external knowledge introduced through Large Language Models (LLMs). Query expansion techniques can improve the accuracy of retrieval systems. The study Introducing external knowledge into the query expansion process is also an effective method of data augmentation. Additionally, This paper investigates the integration of externally generated knowledge from LLMs into query expansion within dense retrieval models. It examines the selection of relevant external knowledge and the effective combination of this knowledge with pseudo-relevant document features. The study evaluates the impact of incorporating external knowledge not only for the original query but also from pseudo-relevant documents, assessing its effect on retrieval performance. The experimental results across two datasets and three evaluation metrics demonstrate that the proposed method, which integrates external knowledge with pseudo-relevant document features, significantly improves the accuracy of the retrieval system.