Domain Specific Machine Translation

Domain-specific machine translation (DSMT) focuses on adapting machine translation models to perform optimally within specialized fields, addressing the limitations of general-purpose models in handling domain-specific terminology and nuances. Current research emphasizes leveraging large language models (LLMs) through fine-tuning techniques, prompt engineering (including dictionary-based and phrase-level prompting), and efficient data selection strategies to improve translation accuracy and reduce training costs. These advancements are crucial for improving applications in diverse sectors like healthcare and finance, where accurate translation of specialized texts is essential, and for addressing low-resource language scenarios.