  Large Language Models (LLM) have demonstrated their strong ability in the
field of machine translation (MT), yet they suffer from high computational cost
and latency. Therefore, transferring translation knowledge from giant LLMs to
medium-sized machine translation models is a promising research direction.
However, traditional knowledge distillation methods do not take the capability
of student and teacher models into consideration, therefore repeatedly teaching
student models on the knowledge they have learned, and failing to extend to
novel contexts and knowledge. In this paper, we propose a framework called
MT-Patcher, which transfers knowledge from LLMs to existing MT models in a
selective, comprehensive and proactive manner. Considering the current
translation ability of student MT models, we only identify and correct their
translation errors, instead of distilling the whole translation from the
teacher. Leveraging the strong language abilities of LLMs, we instruct LLM
teachers to synthesize diverse contexts and anticipate more potential errors
for the student. Experiment results on translating both specific language
phenomena and general MT benchmarks demonstrate that finetuning the student MT
model on about 10% examples can achieve comparable results to the traditional
knowledge distillation method, and synthesized potential errors and diverse
contexts further improve translation performances on unseen contexts and words.
