DenseOn and LateOn: Fully Open Dense and Late-Interaction Models for Multilingual, Long-Context, and Code Search
Score
78论文揭示了不同检索架构在多语言泛化上的差异，并提供了开源资源，信息增量较高。
实验设计严谨，数据构造透明，对比分析清晰，写作规范。
开源模型和数据集可被直接用于多语言检索系统，实用价值高。
当前多语言与开源检索模型正值研究热点，该论文发布于2025年7月，时效性好。
来源为arXiv预印本，但数据构建可复现，代码已开源，信源可信度高。
Base 87 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The paper makes significant contributions with rigorous experiments and abundant open resources, but lacks detailed evaluation on long-context and code search.
AI commentary
「The most notable aspect of this paper is its fully open training recipe for retrieval models, and the discovery of fundamental differences in multilingual transfer between dense and late-interaction models. It is invaluable for researchers in multilingual IR or those who want to reproduce SOTA retrieval models, and also demonstrates how translate-train can achieve cross-lingual generalization for open-source AI ecosystem enthusiasts.」
Quick overview
This research presents a fully open recipe for training retrieval models, reconstructing 665M English contrastive pre-training pairs and training DenseOn (single-vector dense) and LateOn (ColBERT-style late-interaction) models, achieving 56.20 and 57.22 average nDCG@10 on BEIR respectively, setting new SOTA for this parameter size. Extending to eight languages via translate-train yields mDenseOn and mLateOn (307M parameters). The study finds that dense models perform well on translated languages but degrade on unseen languages, while late-interaction models generalize better to unseen languages and scripts, demonstrating that token-level matching turns translate-train from a target-language expansion strategy into a multilingual generalization recipe.
- Reconstructed 665M English contrastive pre-training pairs from 34 public sources and built 1.88M supervised fine-tuning pairs with mined hard negatives, training two 149M-parameter models that achieve SOTA on BEIR for this size class.
- Translated validated English data into eight languages, yielding 2.8B cross-lingual pairs, and trained two 307M-parameter models mDenseOn and mLateOn on mmBERT-base.
- Dense model shows strength on translated English but degrades on unseen languages; late-interaction model generalizes better to unseen languages and scripts, revealing token-level matching turns translate-train into multilingual generalization.
Full text
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