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SARA unlocks the potential of low-resource languages in multilingual models by aligning their expert routing with high-resource anchors, leading to measurable performance gains.
LLMs can learn multilingual translation far more effectively by explicitly separating and routing language modeling and translation knowledge during fine-tuning.
Targeted neuron fine-tuning can unlock superior image translation capabilities in multimodal large language models, outperforming traditional methods by preserving pre-trained knowledge.
Say goodbye to benchmark leakage and inconsistent LLM evals: a new decentralized protocol keeps your data safe while streamlining the evaluation process.
