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.

PLAID 算法与重新排名方法在检索引擎的效率和效果之间存在竞争性权衡，本研究强调在评估检索引擎效率时，需精心选择相关的基线方法。

PLAID 的可重复性研究

A Reproducibility Study of PLAID

Neural information retrieval (IR) has greatly advanced search and other
knowledge-intensive language tasks. While many neural IR methods encode queries
and documents into single-vector representations, late interaction models
produce multi-vector representations at the granularity of each token and
decompose relevance modeling into scalable token-level computations. This
decomposition has been shown to make late interaction more effective, but it
inflates the space footprint of these models by an order of magnitude. In this
work, we introduce ColBERTv2, a retriever that couples an aggressive residual
compression mechanism with a denoised supervision strategy to simultaneously
improve the quality and space footprint of late interaction. We evaluate
ColBERTv2 across a wide range of benchmarks, establishing state-of-the-art
quality within and outside the training domain while reducing the space
footprint of late interaction models by 6--10$\times$.

本研究介绍了 ColBERTv2，结合积极的残差压缩机制和去噪监督策略，以提高对最晚交互的质量和占用空间的同时评估了广泛的基准。