Papers › Exploring ℓ₀ Sparsification for Inference-free Sparse Retrievers
Exploring ℓ₀ Sparsification for Inference-free Sparse Retrievers
Xinjie Shen, Zhichao Geng, Yang Yang
With increasing demands for efficiency, information retrieval has developed a branch of sparse retrieval, further advancing towards inference-free retrieval where the documents are encoded during indexing time and there is no model-inference for queries. Existing sparse retrieval models rely on FLOPS regularization for sparsification, while this mechanism was originally designed for Siamese encoders, it is considered to be suboptimal in inference-free scenarios which is asymmetric. Previous attempts to adapt FLOPS for inference-free scenarios have been limited to rule-based methods, leaving the potential of sparsification approaches for inference-free retrieval models largely unexplored. In this paper, we explore ℓ₀ inspired sparsification manner for inference-free retrievers. Through comprehensive out-of-domain evaluation on the BEIR benchmark, our method achieves state-of-the-art performance among inference-free sparse retrieval models and is comparable to leading Siamese sparse retrieval models. Furthermore, we provide insights into the trade-off between retrieval effectiveness and computational efficiency, demonstrating practical value for real-world applications.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Zero Shot on BEIR (Inference Free Model) | BEIR | $\ell_0$ Mask | NCDG@10 | 50.43 | #1 of 6 | Archive leaderboard | report |
| Zero Shot on BEIR (Inference Free Model) | BEIR | $\ell_0$ Mask-$\ell_0$ Activation | NCDG@10 | 50.28 | #2 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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