Terminology Translation Optimization Program
To improve the accuracy of terminology translation in specialized fields such as science and technology and medicine, it can be achieved in the following ways:
- Field Tip Enhancement: Add field descriptions such as "[medical]" or "[legal]" prefixes to the input text.
- Glossary preloading: Prepare a cross-reference table of high-frequency terms in the field and provide it to the model as an example before translation.
- Using the Seed-X-PPO version: This version is richer in fine-tuned data in specialized areas, and accuracy in bio-medical areas is improved by 17%
- Adjustment of generation parameters: set temperature=0.3, top_p=0.9 balance creativity and accuracy
Specific implementation steps: 1) download the latest model weights via HuggingFace; 2) process long documents using batch inference; 3) perform automatic terminology consistency checking on the results. For particularly critical documents, it is recommended to use a hybrid workflow of manual + AI, where the initial flip is done by the model first, and then proofreading is done by domain experts.
This answer comes from the articleSeed-X-7B: Efficient Multilingual Translation of Large ModelsThe
May not be reproduced without permission:AI productivity tools " How to optimize the effectiveness of translations for multi-domain terminology?
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