MIMO is a two-stage distillation-plus-contrastive framework that anchors multilingual embeddings to a monolingual English space and outperforms prior cross-lingual baselines on MLIR and multi-monolingual benchmarks.
arXiv preprint arXiv:1910.11856 , year=
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LIMO achieves 63.3% on AIME24 and 95.6% on MATH500 via supervised fine-tuning on roughly 1% of the data used by prior models, supporting the claim that minimal strategic examples suffice when pre-training has already encoded domain knowledge.
A tutorial synthesizing foundations, recent models such as PALO and Maya, and low-cost methods for tri-modal multilingual AI in resource-constrained settings.
citing papers explorer
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MIMO: Multilingual Information Retrieval via Monolingual Objectives
MIMO is a two-stage distillation-plus-contrastive framework that anchors multilingual embeddings to a monolingual English space and outperforms prior cross-lingual baselines on MLIR and multi-monolingual benchmarks.
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LIMO: Less is More for Reasoning
LIMO achieves 63.3% on AIME24 and 95.6% on MATH500 via supervised fine-tuning on roughly 1% of the data used by prior models, supporting the claim that minimal strategic examples suffice when pre-training has already encoded domain knowledge.
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Multilingual and Multimodal LLMs in the Wild: Building for Low-Resource Languages
A tutorial synthesizing foundations, recent models such as PALO and Maya, and low-cost methods for tri-modal multilingual AI in resource-constrained settings.