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Recent studies have introduced innovative approaches to improve the multilingual performance of large language models (LLMs). Techniques such as incorporating cross-lingual supervision during pre-training, focusing on high-quality parallel data, and multilingual fine-tuning with translation instructions have shown promise in boosting translation accuracy across diverse languages. These developments address challenges in low-resource language translation and aim to create more inclusive and effective AI communication tools.
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