Using translation memories (TMs) as prompts is a promising approach to
in-context learning of machine translation models. In this work, we take a step
towards prompting large language models (LLMs) with TMs and making them better
translators. We find that the ability of LLMs to ``understand'' prompts is
indeed helpful for making better use of TMs. Experiments show that the results
of a pre-trained LLM translator can be greatly improved by using high-quality
TM-based prompts. These results are even comparable to those of the
state-of-the-art NMT systems which have access to large-scale in-domain
bilingual data and are well tuned on the downstream tasks.Comment: Accepted to Findings of ACL 202