Multilingual and cross-lingual capabilities significantly boost the flexibility and usefulness of large language models (LLMs). Using Qwen as an example, we'll explore methods to enhance multilingual performance in LLMs, including pre-training, post-training, and evaluation strategies. Additionally, we'll examine the real-world applications of these advancements, demonstrating how multilingual capabilities can create practical solutions that overcome language barriers and promote smooth communication.
