arXiv:2606.18811v1 Announce Type: cross Abstract: Learned sparse retrieval (LSR) models such as SPLADE have traditionally used BERT-style masked language models as backbone encoders. A natural expectation is that replacing BERT with stronger pretrained encoders should improve retrieval effectiveness. However, we find that under standard SPLADE training recipes, backbones with large MLM-head L2 norms can suffer performance degradation and even training collapse under standard SPLADE training recipes. We identify this failure as a scale mismatch in the MLM head: SPLADE directly uses MLM-head out
This research addresses a problem encountered when attempting to integrate stronger pretrained language models into learned sparse retrieval frameworks, as these models become increasingly sophisticated.
Improving neural sparse retrieval effectiveness is crucial for advancing search, recommendation, and information retrieval systems across many AI applications.
This research identifies a specific scaling issue in MLM-head integration for sparse retrieval, suggesting that current methods for leveraging advanced backbones need adjustment to achieve expected performance gains.
- · AI researchers and developers
- · Companies with large information retrieval needs
- · Generative AI applications
- · Neural search engines
- · Naive adoption of new large language models in retrieval systems
More robust and effective neural sparse retrieval models will emerge from this understanding.
Improved retrieval capabilities will enhance the performance of AI agents and large language models that rely on RAG (Retrieval Augmented Generation) architectures.
The ability to efficiently search and retrieve vast amounts of information could accelerate scientific discovery and enterprise knowledge management.
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Read at arXiv cs.AI