Adaptive Machine Translation

Adaptive machine translation (AMT) focuses on improving machine translation's ability to dynamically adjust to specific domains or styles, ensuring consistent and accurate translations even with limited in-domain data. Current research heavily utilizes large language models (LLMs), leveraging their in-context learning capabilities to adapt translation outputs at inference time through techniques like fine-tuning or few-shot prompting with domain-specific examples. This approach shows promise in enhancing translation quality, particularly for low-resource languages, and offers significant potential for improving the efficiency and accuracy of real-world translation applications across various fields.