PLAID SHIRTTT for Large-Scale Streaming Dense Retrieval

Dawn Lawrie, Efsun Kayi, Eugene Yang, James Mayfield, Douglas W. Oard

Abstract:

PLAID, an efficient implementation of the ColBERT late interaction
bi-encoder using pretrained language models for ranking, consistently achieves state-of-the-art performance in monolingual, crosslanguage, and multilingual retrieval. PLAID differs from ColBERT
by assigning terms to clusters and representing those terms as cluster centroids plus compressed residual vectors. While PLAID is effective in batch experiments, its performance degrades in streaming
settings where documents arrive over time because representations
of new tokens may be poorly modeled by the earlier tokens used
to select cluster centroids. PLAID Streaming Hierarchical Indexing that Runs on Terabytes of Temporal Text (PLAID SHIRTTT)
addresses this concern using multi-phase incremental indexing
based on hierarchical sharding. Experiments on ClueWeb09 and the
multilingual NeuCLIR collection demonstrate the effectiveness of
this approach both for the largest collection indexed to date by the
ColBERT architecture and in the multilingual setting, respectively.