GRM: Generative Relevance Modeling Using Relevance-Aware Sample Estimation for Document Retrieval
Year: 2023
Type: preprint
Abstract: Recent studies show that Generative Relevance Feedback (GRF), using text generated by Large Language Models (LLMs), can enhance the effectiveness of query expansion. However, LLMs can generate irrelevant information that harms retrieval effectiveness. To address this, we propose Generative Relevance Modeling (GRM) that uses Relevance-Aware Sample Estimation (RASE) for more accurate weighting of expansion terms. Specifically, we identify similar real documents for each generated document and use a neural re-ranker to estimate their relevance. Experiments on three standard document ranking benchmarks show that GRM improves MAP by 6-9% and R@1k by 2-4%, surpassing previous methods.
Source: arXiv (Cornell University)
Authors: Mackie, Iain, Sekulic, Ivan, Chatterjee, Shubham, Dalton, Jeffrey, Crestani, Fabio
Institutions: University of Glasgow, Università della Svizzera italiana
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4381253499