Parker Carlson

Parker Carlson

PhD Student at UC Santa Barbara

Invited Talks

Efficient Sparse Retrieval with Lightweight Superblock Pruning

July 24, 2026

ReNeuIR '26, Melbourne, AUS

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.

Efficient Sparse Retrieval with Lightweight Superblock Pruning

July 23, 2026

SIGIR '26, Melbourne, AUS

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.

Adaptive Sparsity Optimization with Learnable Soft Top-K and Per-Term Thresholding for Efficient Retrieval

July 23, 2026

SIGIR '26, Melbourne, AUS

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.

Modern Index Design for Efficient Learned Sparse Retrieval

March 16, 2026

Invited talk at Seltz

Large-scale search engines typically follow a retrieve-and-rerank paradigm, where an efficient retriever identifies a small set of promising documents that can be reranked using more expensive methods. Learned sparse retrieval (LSR) has emerged as a popular method for first-stage retrieval because it combines the semantic matching abilities of language models with efficient CPU-friendly algorithms based on the inverted index data structure. However, LSR has significantly different distributions of term weights compared to prior exact-match lexical retrieval models which makes many existing optimizations ineffective. Recent work has used a clustering paradigm to group similar documents and prune large portions of the index at query time, while simultaneously leveraging the SIMD capabilities of modern CPUs. In this talk, I will present recent advances in index design for LSR and my work on exact and approximate query processing to make LSR applicable to large-scale datasets.