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PLAID-PRF enhances dense retrieval with centroid-aware pseudo-relevance feedback

Researchers have introduced PLAID-PRF, a novel method for enhancing multi-vector dense retrieval models like ColBERT. This technique leverages centroid-like tokens within the PLAID framework to perform Pseudo-Relevance Feedback (PRF), effectively reformulating query vectors based on top-retrieved results. PLAID-PRF maintains low computational costs by utilizing internal PLAID centroid vectors, demonstrating significant improvements in retrieval effectiveness across various benchmarks, including MS MARCO and BEIR. AI

IMPACT Improves retrieval effectiveness and efficiency in dense retrieval models, potentially impacting search engine performance and information access.

RANK_REASON The cluster describes a new research paper detailing a novel method for information retrieval.

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PLAID-PRF enhances dense retrieval with centroid-aware pseudo-relevance feedback

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COVERAGE [3]

  1. arXiv cs.CL TIER_1 English(EN) · Xiao Wang, Sean MacAvaney, Craig Macdonald ·

    PLAID-PRF: Pseudo-Relevance Feedback with Centroid-like Tokens in PLAID

    arXiv:2607.18626v1 Announce Type: cross Abstract: Multi-vector dense retrieval models, such as ColBERT, achieve strong retrieval effectiveness by modelling fine-grained token-level interactions between queries and documents. Methods such as PLAID use centroid-based quantisation o…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Craig Macdonald ·

    PLAID-PRF: Pseudo-Relevance Feedback with Centroid-like Tokens in PLAID

    Multi-vector dense retrieval models, such as ColBERT, achieve strong retrieval effectiveness by modelling fine-grained token-level interactions between queries and documents. Methods such as PLAID use centroid-based quantisation of each token's vector to reduce the index size and…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    PLAID-PRF: Pseudo-Relevance Feedback with Centroid-like Tokens in PLAID

    Multi-vector dense retrieval models, such as ColBERT, achieve strong retrieval effectiveness by modelling fine-grained token-level interactions between queries and documents. Methods such as PLAID use centroid-based quantisation of each token's vector to reduce the index size and…