# A Static Pruning Study on Sparse Neural Retrievers

arXiv: https://arxiv.org/abs/2304.12702 · HTML: https://mlsanity.com/paper/2304.12702/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2023.

## Built on by (most cited first, 11 of 11)

- [DocPruner: A Storage-Efficient Framework for Multi-Vector Visual Document Retrieval via Adaptive Patch-Level Embedding Pruning](https://mlsanity.com/paper/2509.23883/) (2025)
- [DiSCo: LLM Knowledge Distillation for Efficient Sparse Retrieval in Conversational Search](https://mlsanity.com/paper/2410.14609/) (2024)
- [Learning Retrieval Models with Sparse Autoencoders](https://mlsanity.com/paper/2603.13277/) (2026)
- [The Role of Vocabularies in Learning Sparse Representations for Ranking](https://mlsanity.com/paper/2509.16621/) (2025)
- [An Alternative to FLOPS Regularization to Effectively Productionize SPLADE-Doc](https://mlsanity.com/paper/2505.15070/) (2025)
- [On the Challenges and Opportunities of Learned Sparse Retrieval for Code](https://mlsanity.com/paper/2603.22008/) (2026)
- [Efficient Sketching and Nearest Neighbor Search Algorithms for Sparse Vector Sets](https://mlsanity.com/paper/2509.24815/) (2025)
- [Efficiency and Effectiveness of SPLADE Models on Billion-Scale Web Document Title](https://mlsanity.com/paper/2511.22263/) (2025)
- [AmharicIR+Instr: A Two-Dataset Resource for Neural Retrieval and Instruction Tuning](https://mlsanity.com/paper/2602.09914/) (2026)
- [The Pre-Training Study of Expanded-SPLADE Models on Web Document Titles](https://mlsanity.com/paper/2605.01407/) (2026)
- [Static Pruning Across Sparse Retrieval Regimes: What Transfers, What Breaks, and What Still Helps](https://mlsanity.com/paper/2608.16309/) (2026)

## Abstract (arXiv)

Sparse neural retrievers, such as DeepImpact, uniCOIL and SPLADE, have been introduced recently as an efficient and effective way to perform retrieval with inverted indexes. They aim to learn term importance and, in some cases, document expansions, to provide a more effective document ranking compared to traditional bag-of-words retrieval models such as BM25. However, these sparse neural retrievers have been shown to increase the computational costs and latency of query processing compared to their classical counterparts. To mitigate this, we apply a well-known family of techniques for boosting the efficiency of query processing over inverted indexes: static pruning. We experiment with three static pruning strategies, namely document-centric, term-centric and agnostic pruning, and we assess, over diverse datasets, that these techniques still work with sparse neural retrievers. In particular, static pruning achieves 2× speedup with negligible effectiveness loss (≤ 2% drop) and, depending on the use case, even 4× speedup with minimal impact on the effectiveness (≤ 8% drop). Moreover, we show that neural rerankers are robust to candidates from statically pruned indexes.