PLAID: An Efficient Engine for Late Interaction Retrieval (2205.09707v1)

Abstract: Pre-trained LLMs 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.

• The paper introduces PLAID, a novel retrieval engine that leverages centroid-based interaction to efficiently filter and rank candidates in late interaction models.
• It achieves latency reductions of up to 7× on GPUs and 45× on CPUs while maintaining high retrieval quality.
• The research demonstrates PLAID’s scalability across large datasets like MS MARCO and Wikipedia, paving the way for practical, real-time IR applications.

An Analysis of "PLAID: An Efficient Engine for Late Interaction Retrieval"

The paper "PLAID: An Efficient Engine for Late Interaction Retrieval" introduces a novel framework aimed at optimizing search latency in information retrieval systems that utilize pre-trained LLMs. This work, authored by researchers from Stanford University, is particularly focused on refining the late interaction mechanism exemplified in models such as ColBERT and its enhanced version, ColBERTv2. The core contribution of this research is the Performance-optimized Late Interaction Driver (PLAID), which leverages centroid-based interaction and pruning mechanisms to significantly reduce search latency without sacrificing retrieval quality.

Core Contributions

PLAID is introduced as a retrieval engine designed to address the computational inefficiencies inherent in late interaction models. The authors harness a centroid interaction strategy that treats each document as a "bag of centroids." This approach allows for rapid elimination of low-scoring passages, thus achieving considerable reductions in search latency—up to 7× on a GPU and 45× on a CPU—relative to vanilla ColBERTv2.

The framework operates by representing documents and queries via token-level vectors, which are encoded as centroids using k-means clustering. By implementing a multi-stage filtering and ranking process, PLAID leverages these centroids to perform preliminary retrieval and rank optimization. This multi-stage process is crucial in maintaining high recall with reduced computational load by sparing the need to decompress and process full vector representations for non-promising candidates.

Technical Insights

1. Centroid Interaction and Pruning: PLAID's innovation lies in employing centroids to approximate the document relevance for candidate selection. In the first stage, passages linked to highly relevant centroids are selected without decompressing their residuals. Subsequent stages introduce centroid pruning to discard less relevant centroids, thereby focusing computation on a refined set.
2. Optimized Kernels: The implementation of PLAID features optimized kernels for data movement and scoring, which are integral to its performance enhancements. By adopting a padding-free MaxSim computation and an optimized decomposition strategy, the paper demonstrates substantial latency reductions on both CPU and GPU platforms.
3. Scalability and Evaluation: The research extensively evaluates PLAID across several datasets, including MS MARCO v1 and v2, Wikipedia, and LoTTE, proving its efficacy across different scales—up to 140 million passages. The results consistently indicate that PLAID matches or slightly improves retrieval quality while drastically minimizing latency.

Implications and Future Directions

PLAID stands as a significant development in improving the efficiency of retrieval systems that require detailed query-document interactions. Its ability to retain quality while enhancing scalability paves the way for more robust applications in large-scale search tasks. The focus on centroid interactions reflects a promising direction for reducing computational overheads while maintaining the granularity of token-level interactions.

Future explorations could delve into broader optimization strategies, potentially incorporating further machine learning techniques for centroid selection, or even extending the work to accommodate increasingly complex LLMs. The paper aligns with ongoing efforts to balance quality and efficiency in neural IR models, underscoring the necessity to adapt retrieval systems for ever-growing datasets and real-time applications.

In summary, PLAID offers a methodological leap in the landscape of late interaction retrieval by optimizing both algorithmic design and computational implementation, setting a benchmark for future IR systems focused on latency-sensitive environments.