# Representation Sparsification with Hybrid Thresholding for Fast SPLADE-based Document Retrieval

arXiv: https://arxiv.org/abs/2306.11293 · HTML: https://mlsanity.com/paper/2306.11293/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2023.

## Built on by (most cited first, 2 of 2)

- [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)

## Abstract (arXiv)

Learned sparse document representations using a transformer-based neural model has been found to be attractive in both relevance effectiveness and time efficiency. This paper describes a representation sparsification scheme based on hard and soft thresholding with an inverted index approximation for faster SPLADE-based document retrieval. It provides analytical and experimental results on the impact of this learnable hybrid thresholding scheme.