Technical Implementation of Hundred Languages Understanding and Generation
The model achieves superior multilingual competence through three phases of training: 1) basic semantic space construction 2) cross-language alignment enhancement 3) culturally adaptive fine-tuning. Key techniques include:
- Dynamic vocabulary expansion to support rare language characters
- Language Family Sharing Parameters Improve Resource Utilization
- Cultural sensitivity filters to avoid cross-cultural conflict
In the FLORES-101 evaluation, the model's BLEU value on major language pairs such as Chinese/English/Japanese reaches 68-72, and the translation quality of minor languages (e.g., Kiswahili) is improved by 40% compared with the traditional scheme.Typical applications include the simultaneous generation of multi-language for multinational company documents, real-time customer service response for cross-border e-commerce, and other scenarios.
This answer comes from the articleQwen3-235B-A22B-Thinking-2507: A large-scale language model to support complex reasoningThe
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