Generative Relevance Feedback with Large Language Models
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.
Code (0)
등록된 구현이 없습니다.
Tasks
ArticlesLanguage ModelingLanguage ModellingRetrievalSimilar Papers 제목 키워드 기반
Generative Relevance Feedback and Convergence of Adaptive Re-Ranking: University of Glasgow Terrier Team at TREC DL 2023
This paper describes our participation in the TREC 2023 Deep Learning Track. We submitted runs that apply generative relevance feedback from a large language model in both a zero-shot and pseudo-relevance feedback settin…
Language ModelingLanguage ModellingLarge Language ModelRe-Ranking+1A Little More Like This: Text-to-Image Retrieval with Vision-Language Models Using Relevance Feedback
Large vision-language models (VLMs) enable intuitive visual search using natural language queries. However, improving their performance often requires fine-tuning and scaling to larger model variants. In this work, we pr…
Natural Language QueriesImage RetrievalGenerative and Pseudo-Relevant Feedback for Sparse, Dense and Learned Sparse Retrieval
Pseudo-relevance feedback (PRF) is a classical approach to address lexical mismatch by enriching the query using first-pass retrieval. Moreover, recent work on generative-relevance feedback (GRF) shows that query expansi…
Document RankingRetrievalGRM: Generative Relevance Modeling Using Relevance-Aware Sample Estimation for Document Retrieval
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…
Document RankingRetrievalGQE-PRF: Generative Query Expansion with Pseudo-Relevance Feedback
Query expansion with pseudo-relevance feedback (PRF) is a powerful approach to enhance the effectiveness in information retrieval. Recently, with the rapid advance of deep learning techniques, neural text generation has …
Information RetrievalRerankingRetrievalText Generation