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Pseudo-relevance feedback (PRF) is a classical approach to address lexical mismatch by enriching the query using first-pass retrieval.
Relevance feedback in information retrieval
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Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls
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ColBERT-PRF: Semantic Pseudo-Relevance Feedback for Dense Passage and Document Retrieval
Xiao Wang, Craig Macdonald, Nicola Tonellotto, and Iadh Ounis. 2022 · 2022
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ExaRanker: Explanation-Augmented Neural Ranker
Fernando Ferraretto, Thiago Laitz, Roberto Lotufo, and Rodrigo Nogueira. 2023 · 2023
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InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval
Vitor Jeronymo, Luiz Bonifacio, Hugo Abonizio, Marzieh Fadaee, Roberto Lotufo, Jakub Zavrel, and Rodrigo Nogueira. 2023 · 2023
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Naver Labs Europe (SPLADE)@ TREC Deep Learning 2022
Carlos Lassance and Stéphane Clinchant. 2023 · 2023
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Generative Relevance Feedback with Large Language Models
Iain Mackie, Shubham Chatterjee, and Jeffrey Dalton. 2023 · 2023
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Adapting Learned Sparse Retrieval for Long Documents
Thong Nguyen, Sean MacAvaney, and Andrew Yates. 2023 · 2023
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Visconde: Multi-document QA with GPT-3 and Neural Reranking. In Advances in Information Retrieval: 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, April 2–6, 2023, Proceedings, Part II . Springer, 534–543
Jayr Pereira, Robson Fidalgo, Roberto Lotufo, and Rodrigo Nogueira. 2023 · 2023
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ColBERT-PRF: Semantic Pseudo-Relevance Feedback for Dense Passage and Document Retrieval
Xiao Wang, Craig Macdonald, Nicola Tonellotto, and Iadh Ounis. 2023 · 2023
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