Enhancing Machine Translation Quality through Large Language Model-based Post-Editing with Error Annotations

Leveraging the complementary strengths of large language models (LLMs) and supervised machine translation (MT) systems, this work explores strategies to guide LLaMA-2 models to improve MT outputs using external feedback on translation errors.