Strategies for Improving the Quality of Translations into Low-Resource Languages
The following measures can be taken for translation optimization for low-resource languages:
- Taking Advantage of Multilingual Co-Training of Models: Seed-X-7B enables knowledge transfer from high-resource languages to low-resource languages through cross-language pre-training
- Adding Language Identifiers: Explicitly specify the target language label in the input text (e.g., for Swahili) to help the model accurately identify the direction of translation
- Enable Beam Search decoding: Setting beam_width=4 produces more stable output in low-resource languages
- post-editing process: Use regularized filtering on the output to correct common morphological errors
In practice, it is recommended to 1) test different TEMPERATURE parameters (between 0 and 1), 2) perform manual calibration for critical content, and 3) collect error samples to feed back to the development team for continuous model improvement. The list of languages supported by the model contains a variety of low-resource languages, which can be significantly improved by appropriate hint engineering.
This answer comes from the articleSeed-X-7B: Efficient Multilingual Translation of Large ModelsThe
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