ML-Embed: Inclusive and Efficient Embeddings for a Multilingual World
The development of high-quality text embeddings is increasingly drifting toward an exclusionary future, defined by three critical barriers: prohibitive computational costs, a narrow linguistic focus that neglects most of the world's languages, and a lack of transparency from closed-source or open-weight models that stifles research. To dismantle these barriers, we introduce ML-Embed, a suite of inclusive and efficient models built upon a new framework: 3-Dimensional Matryoshka Learning (3D-ML). Our framework addresses the computational challenge with comprehensive efficiency across the entire model lifecycle. Beyond the storage benefits of Matryoshka Representation Learning (MRL) and flex...
| Authors | Ziyin Zhang, Zihan Liao, Hang Yu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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