  Current query expansion models use pseudo-relevance feedback to improve
first-pass retrieval effectiveness; however, this fails when the initial
results are not relevant. Instead of building a language model from retrieved
results, we propose Generative Relevance Feedback (GRF) that builds
probabilistic feedback models from long-form text generated from Large Language
Models. We study the effective methods for generating text by varying the
zero-shot generation subtasks: queries, entities, facts, news articles,
documents, and essays. We evaluate GRF on document retrieval benchmarks
covering a diverse set of queries and document collections, and the results
show that GRF methods significantly outperform previous PRF methods.
Specifically, we improve MAP between 5-19% and NDCG@10 17-24% compared to RM3
expansion, and achieve the best R@1k effectiveness on all datasets compared to
state-of-the-art sparse, dense, and expansion models.
