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, cross-language, 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.

PLAID SHIRTTT 是 ColBERT 的扩展版本，使用预训练语言模型实现了高效的排名。通过多阶段的增量索引和层次分片，解决了 PLAID 在流式场景下性能下降的问题，并在不同规模和多语言环境下进行了有效性验证。

大规模流式密集检索的格子衬衫

PLAID SHIRTTT for Large-Scale Streaming Dense Retrieval

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

The HLTCOE team applied PLAID, an mT5 reranker, and document translation to
the TREC 2023 NeuCLIR track. For PLAID we included a variety of models and
training techniques -- the English model released with ColBERT v2,
translate-train~(TT), Translate Distill~(TD) and multilingual
translate-train~(MTT). TT trains a ColBERT model with English queries and
passages automatically translated into the document language from the MS-MARCO
v1 collection. This results in three cross-language models for the track, one
per language. MTT creates a single model for all three document languages by
combining the translations of MS-MARCO passages in all three languages into
mixed-language batches. Thus the model learns about matching queries to
passages simultaneously in all languages. Distillation uses scores from the mT5
model over non-English translated document pairs to learn how to score
query-document pairs. The team submitted runs to all NeuCLIR tasks: the CLIR
and MLIR news task as well as the technical documents task.

HLTCOE 团队在 TREC 2023 NeuCLIR 跟踪中应用了 PLAID，mT5 reranker 和文档翻译。

HLTCOE 参加 TREC 2023 NeuCLIR 赛道

HLTCOE at TREC 2023 NeuCLIR Track

Pre-trained language models are increasingly important components across
multiple information retrieval (IR) paradigms. Late interaction, introduced
with the ColBERT model and recently refined in ColBERTv2, is a popular paradigm
that holds state-of-the-art status across many benchmarks. To dramatically
speed up the search latency of late interaction, we introduce the
Performance-optimized Late Interaction Driver (PLAID). Without impacting
quality, PLAID swiftly eliminates low-scoring passages using a novel centroid
interaction mechanism that treats every passage as a lightweight bag of
centroids. PLAID uses centroid interaction as well as centroid pruning, a
mechanism for sparsifying the bag of centroids, within a highly-optimized
engine to reduce late interaction search latency by up to 7$\times$ on a GPU
and 45$\times$ on a CPU against vanilla ColBERTv2, while continuing to deliver
state-of-the-art retrieval quality. This allows the PLAID engine with ColBERTv2
to achieve latency of tens of milliseconds on a GPU and tens or just few
hundreds of milliseconds on a CPU at large scale, even at the largest scales we
evaluate with 140M passages.

本研究通过引入性能优化的虚拟中心点交互机制，构建一个名为 PLAID 的优化 late interaction 模型引擎，极大地加速 late interaction 搜索延迟，同时保持准确度。通过在 GPU 和 CPU 上的测试表明，该引擎在大规模情况下甚至可以实现毫秒级的搜索延迟。