Document clustering is a common technique used in sparse retrieval to group similar documents together. The K-means method has been widely adopted to group similar sparse vectors together, but it is not scalable when dealing with a large number of clusters, and the bipartite graph partitioning (BP) method is a preferred choice for block-based document retrieval to partition a large document set efficiently. This paper revisits such a partitioning approach and proposes a balanced K-means-guided bisection method with log-linear complexity while maintaining a good similarity-based clustering quality. Our evaluation with several IR datasets for block-based sparse retrieval algorithms show that the proposed method outperforms the baseline variants of K-means in scalability with 29-414x faster partitioning even for small datasets and reduces retrieval latency by up to 32% compared to BP when clustering 8.8M MS MARCO passages using SPLADE++ embeddings.

Learned sparse retrieval (LSR) is a popular method for first-stage retrieval because it combines the semantic matching of language models with efficient CPU-friendly algorithms. Previous work aggregates blocks into ''superblocks'' to quickly skip the visitation of blocks during query processing by using an advanced pruning heuristic. This paper proposes a simple and effective superblock pruning scheme that reduces the overhead of superblock score computation while preserving competitive relevance. It combines this scheme with a compact index structure and a robust zero-shot configuration that is effective across LSR models and multiple datasets. This paper provides an analytical justification and evaluation on the MS MARCO and BEIR datasets, demonstrating that the proposed scheme can be a strong alternative for efficient sparse retrieval.

Recent work on neural sparse retrieval has demonstrated strong relevance by leveraging Large Language Models (LLMs) for semantic term expansion. However, learned models paired with previous sparsification techniques still yield overly long document and query vectors partly due to a large LLM vocabulary, imposing a serious challenge to retrieval time and space efficiency. This paper proposes a scheme for optimizing model sparsity through a synergy of adaptive strategies, including learnable soft top-??, per-term thresholding, and FLOPs regularization to increase the sparsity of query and document vectors. Experimental results with Lion-SP model on the MS MARCO and BEIR datasets demonstrate that the proposed scheme can outperform the baselines by significantly reducing the average query and document lengths. Our scheme can achieve much shorter retrieval latency and lower storage cost while maintaining highly competitive relevance.

This paper proposes superblock pruning (SP) during top-k online document retrieval for learned sparse representations. SP structures the sparse index as a set of superblocks on a sequence of document blocks and conducts a superblock-level selection to decide if some superblocks can be pruned before visiting their child blocks. SP generalizes the previous flat block or cluster-based pruning, allowing the early detection of groups of documents that cannot or are less likely to appear in the final top-k list. SP can accelerate sparse retrieval in a rank-safe or approximate manner under a high-relevance competitiveness constraint. Our experiments show that the proposed scheme significantly outperforms state-of-the-art baselines on MS MARCO passages on a single-threaded CPU.

Low-cost retrieval is crucial for document search on resource-limited computing platforms. This paper presents a staged sparse-to-dense retrieval framework that substitutes expensive dense query encoding with a dense pseudo-query (DPQ), an approximation derived solely from sparse retrieval results. DPQ scheme employs a simple, rank-aware weighting to combine corresponding dense representations of top sparse results, providing an opportunity to efficiently leverage an expensive but expressive LLM or BERT-based dense model without requiring GPUs. The evaluation demonstrates that DPQ-based retrieval runs fast on an affordable platform and outperforms several low-cost baselines in zero-shot retrieval.

This paper revisits dynamic pruning through rank score thresholding in cluster-based sparse retrieval to skip the index partially at cluster and document levels during inference.It proposes a two-parameter pruning control scheme called ASC with a probabilistic guarantee on rank-safeness competitiveness.ASC uses cluster-level maximum weight segmentation to improve accuracy of rank score bound estimation and threshold-driven pruning, and is targeted for speeding up retrieval applications requiring high relevance competitiveness.The experiments with MS MARCO and BEIR show that ASC improves the accuracy and safeness of pruning for better relevance while delivering a low latency on a single-threaded CPU.

Previous work has demonstrated the potential to combine document rankings from dense and sparse retrievers for higher relevance effectiveness. This paper proposes a cluster-based partial dense retrieval scheme guided by sparse retrieval results to optimize fusion between dense and sparse retrieval at a low space and CPU-time cost while retaining a competitive relevance. This scheme exploits the overlap of sparse retrieval results and document embedding clusters, and judiciously selects a limited number of clusters to probabilistically guarantee the inclusion of top sparse results. This paper provides an evaluation of this scheme on its in-domain and zero-shot retrieval performance for the MS MARCO and BEIR datasets.