Abstract
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 expansion models using text generated from large language models can improve sparse retrieval without depending on first-pass retrieval effectiveness. This work extends GRF to dense and learned sparse retrieval paradigms with experiments over six standard document ranking benchmarks. We find that GRF improves over comparable PRF techniques by around 10% on both precision and recall-oriented measures. Nonetheless, query analysis shows that GRF and PRF have contrasting benefits, with GRF providing external context does not present in first-pass retrieval, whereas PRF grounds the query to the information contained within the target corpus. Thus, we propose combining generative and pseudo-relevance feedback ranking signals to achieve the benefits of both feedback classes, which significantly increases recall over PRF methods on 95% of experiments.
Recommended Citation
I. Mackie et al., "Generative and Pseudo-Relevant Feedback for Sparse, Dense and Learned Sparse Retrieval,", May 2023.
Department(s)
Computer Science
Keywords and Phrases
Pseudo-Relevance Feedback; Text Generation; Document Retrieval
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Publication Date
May 12, 2023
