  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.
