  The PLAID (Performance-optimized Late Interaction Driver) algorithm for
ColBERTv2 uses clustered term representations to retrieve and progressively
prune documents for final (exact) document scoring. In this paper, we reproduce
and fill in missing gaps from the original work. By studying the parameters
PLAID introduces, we find that its Pareto frontier is formed of a careful
balance among its three parameters; deviations beyond the suggested settings
can substantially increase latency without necessarily improving its
effectiveness. We then compare PLAID with an important baseline missing from
the paper: re-ranking a lexical system. We find that applying ColBERTv2 as a
re-ranker atop an initial pool of BM25 results provides better
efficiency-effectiveness trade-offs in low-latency settings. However,
re-ranking cannot reach peak effectiveness at higher latency settings due to
limitations in recall of lexical matching and provides a poor approximation of
an exhaustive ColBERTv2 search. We find that recently proposed modifications to
re-ranking that pull in the neighbors of top-scoring documents overcome this
limitation, providing a Pareto frontier across all operational points for
ColBERTv2 when evaluated using a well-annotated dataset. Curious about why
re-ranking methods are highly competitive with PLAID, we analyze the token
representation clusters PLAID uses for retrieval and find that most clusters
are predominantly aligned with a single token and vice versa. Given the
competitive trade-offs that re-ranking baselines exhibit, this work highlights
the importance of carefully selecting pertinent baselines when evaluating the
efficiency of retrieval engines.
