Multi-vector retrieval methods such as ColBERT and its recent variant, the ConteXtualized Token Retriever (XTR), offer high accuracy but face efficiency challenges at scale. To address this, we present WARP, a retrieval engine that substantially improves the efficiency of retrievers trained with the XTR objective through three key innovations: (1) WARPSELECT for dynamic similarity imputation; (2) implicit decompression, avoiding costly vector reconstruction during retrieval; and (3) a two-stage reduction process for efficient score aggregation. Combined with highly-optimized C++ kernels, our system reduces end-to-end latency compared to XTR's reference implementation by 41x, and achieves a 3x speedup over the ColBERTv2/PLAID engine, while preserving retrieval quality. WARP also reduces index sizes by a factor of 2x-4x compared to XTR, enabling deployment on memory-constrained devices.

Recent progress in neural information retrieval has demonstrated large gains in quality, while often sacrificing efficiency and interpretability compared to classical approaches. We propose ColBERTer, a neural retrieval model using contextualized late interaction (ColBERT) with enhanced reduction. Along the effectiveness Pareto frontier, ColBERTer dramatically lowers ColBERT's storage requirements while simultaneously improving the interpretability of its token-matching scores. To this end, ColBERTer fuses single-vector retrieval, multi-vector refinement, and optional lexical matching components into one model. For its multi-vector component, ColBERTer reduces the number of stored vectors by learning unique whole-word representations and learning to identify and remove word representations that are not essential to effective scoring. We employ an explicit multi-task, multi-stage training to facilitate using very small vector dimensions. Results on the MS MARCO and TREC-DL collection show that ColBERTer reduces the storage footprint by up to 2.5x, while maintaining effectiveness. With just one dimension per token in its smallest setting, ColBERTer achieves index storage parity with the plaintext size, with very strong effectiveness results. Finally, we demonstrate ColBERTer's robustness on seven high-quality out-of-domain collections, yielding statistically significant gains over traditional retrieval baselines.

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) engine. 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 7x on a GPU and 45x 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.

Neural information retrieval systems typically use a cascading pipeline, in which a first-stage model retrieves a candidate set of documents and one or more subsequent stages re-rank this set using contextualized language models such as BERT. In this paper, we propose DeepImpact, a new document term-weighting scheme suitable for efficient retrieval using a standard inverted index. Compared to existing methods, DeepImpact improves impact-score modeling and tackles the vocabulary-mismatch problem. In particular, DeepImpact leverages DocT5Query to enrich the document collection and, using a contextualized language model, directly estimates the semantic importance of tokens in a document, producing a single-value representation for each token in each document. Our experiments show that DeepImpact significantly outperforms prior first-stage retrieval approaches by up to 17% on effectiveness metrics w.r.t. DocT5Query, and, when deployed in a re-ranking scenario, can reach the same effectiveness of state-of-the-art approaches with up to 5.1x speedup in efficiency.

Many top-k document retrieval strategies have been proposed based on the WAND and MaxScore heuristics and yet, from recent work, it is surprisingly difficult to identify the "fastest" strategy. This becomes even more challenging when considering various retrieval criteria, like different ranking models and values of k. In this paper, we conduct the first extensive comparison between ten effective strategies, many of which were never compared before to our knowledge, examining their efficiency under five representative ranking models. Based on a careful analysis of the comparison, we propose LazyBM, a remarkably simple retrieval strategy that bridges the gap between the best performing WAND-based and MaxScore-based approaches. Empirically, LazyBM considerably outperforms all of the considered strategies across ranking models, values of k, and index configurations under both mean and tail query latency.

Recent progress in Natural Language Understanding (NLU) is driving fast-paced advances in Information Retrieval (IR), largely owed to fine-tuning deep language models (LMs) for document ranking. While remarkably effective, the ranking models based on these LMs increase computational cost by orders of magnitude over prior approaches, particularly as they must feed each query-document pair through a massive neural network to compute a single relevance score. To tackle this, we present ColBERT, a novel ranking model that adapts deep LMs (in particular, BERT) for efficient retrieval. ColBERT introduces a late interaction architecture that independently encodes the query and the document using BERT and then employs a cheap yet powerful interaction step that models their fine-grained similarity. By delaying and yet retaining this fine-granular interaction, ColBERT can leverage the expressiveness of deep LMs while simultaneously gaining the ability to pre-compute document representations offline, considerably speeding up query processing. Crucially, ColBERT's pruning-friendly interaction mechanism enables leveraging vector-similarity indexes for end-to-end retrieval directly from millions of documents. We extensively evaluate ColBERT using two recent passage search datasets. Results show that ColBERT's effectiveness is competitive with existing BERT-based models (and outperforms every non-BERT baseline), while executing two orders-of-magnitude faster and requiring up to four orders-of-magnitude fewer FLOPs per query.

Relational join is a central data management operation that influences the performance of almost every database query. In this paper, we show that different input features and hardware settings necessitate different main-memory hash join models. Subsequently, we identify four particular models by which hash-based join algorithms can be executed and propose a novel polymorphic paradigm that dynamically subscribes to the best model given workload and hardware characteristics. We refer to our polymorphic paradigm as PolyHJ and suggest a corresponding implementation, which consists of two mechanisms, namely, in-place, cache-aware partitioning (ICP) and collaborative building and probing (ColBP). ICP and ColBP serve substantially in reducing multi-core cache misses, memory bandwidth usage, and cross-socket traffic. Our experimental results demonstrate that PolyHJ can successfully select the right models for the tested workloads and significantly outperform the current state-of-the-art hash-based join schemes.

This paper presents LA3 , a scalable distributed system for graph analytics. LA3 couples a vertex-based programming model with a highly optimized linear algebra-based engine. It translates any vertex-centric program into an iteratively executed sparse matrix-vector multiplication (SpMV). To reduce communication and enhance scalability, the adjacency matrix representing an input graph is partitioned into locality-aware 2D tiles distributed across multiple processes. Alongside, three major optimizations are incorporated to preclude redundant computations and minimize communication. First, the link-based structure of the input graph is exploited to classify vertices into different types. Afterwards, vertices of special types are factored out of the main loop of the graph application to avoid superfluous computations. We refer to this novel optimization as computation filtering. Second, a communication filtering mechanism is involved to optimize for the high sparsity of the input matrix due to power-law distributions, common in real-world graphs. This optimization ensures that each process receives only the messages that pertain to non-zero entries in its tiles, substantially reducing communication traffic since most tiles are highly sparse. Lastly, a pseudo-asynchronous computation and communication optimization is proposed, whereby processes progress and communicate asynchronously, consume messages as soon as they become available, and block otherwise. We implemented and extensively tested LA3 on private and public clouds. Results show that LA3 outperforms six related state-of-the-art and popular distributed graph analytics systems by an average of 10X.