PLAID-PRF: Pseudo-Relevance Feedback with Centroid-like Tokens in PLAID
A new method called PLAID-PRF is introduced to enhance multi-vector dense retrieval models by applying pseudo-relevance feedback using centroid-based quantization from PLAID. By selecting a diverse set of expansion vectors from top-ranked results and appending them to the original query, PLAID-PRF improves retrieval effectiveness—achieving up to 4.3% higher nDCG@10 and 7.3% higher MRR@10 compared to PLAID, with minimal computational overhead. The approach is validated on MSMARCO and BEIR benchmarks, showing consistent improvements over existing baselines.
Why it matters: This work provides a practical and efficient way to boost retrieval quality in late-interaction models, which is valuable for advancing search and information retrieval systems.
Full story at: arXiv Information Retrieval ↗