Sunday 06 July 2025
As we delve into the world of information retrieval, a crucial aspect has long been a thorn in the side of researchers: query expansion. This technique involves generating hypothetical queries to bridge the gap between user intent and relevant documents. While various methods have been proposed, none have proven entirely effective.
Enter Exp4Fuse, a novel framework that harnesses the power of large language models (LLMs) to enhance sparse retrieval performance. By simultaneously considering two retrieval routes – one based on the original query and the other on an LLM-augmented query – Exp4Fuse generates two ranked lists using a sparse retriever and then fuses them using a modified reciprocal rank fusion method.
The key innovation lies in the application of zero-shot LLM-based query expansion. Unlike traditional methods that rely on complex prompt strategies and advanced dense retrieval techniques, Exp4Fuse leverages the ability of LLMs to generate hypothetical documents for query expansion. This approach not only simplifies the process but also improves performance by exploiting the language models’ capacity for understanding nuances in human language.
To evaluate the efficacy of Exp4Fuse, researchers conducted extensive experiments on three MS MARCO-related datasets and seven low-resource datasets. The results are nothing short of remarkable: not only does Exp4Fuse outperform existing LLM-based query expansion methods but also, when combined with advanced sparse retrievers, it achieves state-of-the-art performance on several benchmarks.
The implications of Exp4Fuse are far-reaching. By bridging the gap between user intent and relevant documents, this framework has the potential to revolutionize information retrieval in a wide range of applications, from search engines to dialogue systems and recommendation systems. Moreover, its simplicity and effectiveness make it an attractive option for real-world deployment.
One potential avenue for future research lies in exploring the limitations of Exp4Fuse. While the framework demonstrates impressive results, there may be scenarios where the LLM-generated queries fail to capture the nuances of user intent or the sparse retriever struggles to identify relevant documents. Addressing these challenges will require further investigation into the intricacies of human language and the capabilities of LLMs.
In a world where information retrieval is increasingly critical, Exp4Fuse offers a promising solution for improving performance and efficiency. By harnessing the power of large language models, this framework has the potential to transform our understanding of query expansion and, in turn, enhance our ability to extract relevant information from vast databases.
Cite this article: “Exp4Fuse: A Novel Framework for Query Expansion Using Large Language Models”, The Science Archive, 2025.
Query Expansion, Large Language Models, Sparse Retrieval, Ms Marco, Query Intent, Information Retrieval, Dialogue Systems, Recommendation Systems, Search Engines, Natural Language Processing
