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New technique improves SPLADE retrieval models with larger encoders

By PulseAugur Editorial · [2 sources] ·

Researchers have identified a performance degradation issue when using larger, more powerful pretrained encoders with SPLADE, a neural sparse retrieval model. This problem, termed a "scale mismatch" in the MLM head, can lead to unstable training or even collapse. The researchers propose a simple, zero-cost adjustment that rescales the MLM head's projection before training, which significantly improves stability and retrieval effectiveness for models like ModernBERT and Ettin. This correction allows these larger backbones to match or surpass the performance of the classic BERT-SPLADE baseline. AI

IMPACT This research offers a method to enhance the performance of neural sparse retrieval systems by better calibrating larger pretrained models, potentially leading to more effective information retrieval.

RANK_REASON The cluster contains a research paper detailing a novel technique for improving neural sparse retrieval models.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New technique improves SPLADE retrieval models with larger encoders

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Youngjoon Jang, Seongtae Hong, Jonah Turner, Heuiseok Lim ·

    Rescaling MLM-Head for Neural Sparse Retrieval

    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 ret…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Heuiseok Lim ·

    Rescaling MLM-Head for Neural Sparse Retrieval

    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 …

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