arXiv:2604.21511v2 Announce Type: replace-cross 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 continuous evolution of AI models and the increasing complexity of language understanding necessitates more robust and flexible representations beyond basic tokenization.
Improving the underlying semantic representation of information retrieval models like SPLADE can significantly enhance the efficiency and effectiveness of search and data analysis, especially across diverse languages and modalities.
The proposed shift from fixed vocabulary backbones to learned semantic concepts through Sparse Auto-Encoders could lead to more robust, adaptable, and performant AI models in information retrieval.
- · AI/ML researchers
- · Information retrieval developers
- · Multilingual content platforms
- · Data analysis firms
- · Legacy keyword-based search systems
- · Fixed-vocabulary NLP models
Information retrieval systems become more accurate and handle polysemy and synonymy more effectively.
This could accelerate the development of more sophisticated AI agents capable of understanding and synthesizing information across multiple languages and data types.
Improved cross-lingual information retrieval could facilitate better global intelligence analysis and more seamless international data exchange.
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