  Machine Translation (MT) remains one of the last NLP tasks where large
language models (LLMs) have not yet replaced dedicated supervised systems. This
work exploits the complementary strengths of LLMs and supervised MT by guiding
LLMs to automatically post-edit MT with external feedback on its quality,
derived from Multidimensional Quality Metric (MQM) annotations. Working with
LLaMA-2 models, we consider prompting strategies varying the nature of feedback
provided and then fine-tune the LLM to improve its ability to exploit the
provided guidance. Through experiments on Chinese-English, English-German, and
English-Russian MQM data, we demonstrate that prompting LLMs to post-edit MT
improves TER, BLEU and COMET scores, although the benefits of fine-grained
feedback are not clear. Fine-tuning helps integrate fine-grained feedback more
effectively and further improves translation quality based on both automatic
and human evaluation.
