PLAID-PRF: Query Expansion That Reuses What Your Index Already Stores
Summary
Pseudo relevance feedback has been known since the 1970s: run the search, assume the top results are roughly right, pull useful terms out of them, and search again with a better query. It reliably improves ranking, and almost nobody runs it on modern multi-vector retrieval because the second pass costs more than the first. ColBERT keeps one vector per token and scores every query token against every document token, and PLAID makes that affordable by quantising each token vector to its nearest centroid so the index stores centroid identifiers rather than full vectors. PLAID-PRF (Wang, MacAvaney, Macdonald, SIGIR 2026) notices those centroids are already in the index and behave like tokens, so feedback can run on them directly: select a small diverse high utility set and append it to the query, for up to 4.3% better nDCG at 10 without query time clustering. Paper: arxiv.org/abs/2607.18626
About this video
Pseudo relevance feedback has been known since the 1970s: run the search, assume the top results are roughly right, pull useful terms out of them, and search again with a better query. It reliably improves ranking, and almost nobody runs it on modern multi-vector retrieval because the second pass costs more than the first. ColBERT keeps one vector per token and scores every query token against every document token, and PLAID makes that affordable by quantising each token vector to its nearest centroid so the index stores centroid identifiers rather than full vectors. PLAID-PRF (Wang, MacAvaney, Macdonald, SIGIR 2026) notices those centroids are already in the index and behave like tokens, so feedback can run on them directly: select a small diverse high utility set and append it to the query, for up to 4.3% better nDCG at 10 without query time clustering. Paper: arxiv.org/abs/2607.18626