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arXiv · 2604.21511

From Tokens to Concepts: Leveraging SAE for SPLADE

Yuxuan Zong·Mathias Vast·Basile Van Cooten·Laure Soulier·Benjamin Piwowarski
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Abstract

Learned Sparse IR models, such as SPLADE, offer an excellent efficiency-effectiveness tradeoff. However, they rely on the underlying backbone vocabulary, which might hinder performance (polysemicity and synonymy) and pose a challenge for multi-lingual and multi-modal usages. To solve this limitation, we propose to replace the backbone vocabulary with a latent space of semantic concepts learned using Sparse Auto-Encoders (SAE). Throughout this paper, we study the compatibility of these 2 concepts, explore training approaches, and analyze the differences between our SAE-SPLADE model and traditional SPLADE models. Our experiments demonstrate that SAE-SPLADE achieves retrieval performance comparable to SPLADE on both in-domain and out-of-domain tasks while offering improved efficiency.

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Yuxuan Zong, Mathias Vast, Basile Van Cooten, Laure Soulier, Benjamin Piwowarski. 2026-04-23. From Tokens to Concepts: Leveraging SAE for SPLADE. https://arxiv.org/abs/2604.21511

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