Augmenting Large Language Model Translators via Translation Memories
Yongyu Mu Abudurexiti Reheman Zhiquan Cao Yuchun Fan Bei Li Yinqiao Li Tong Xiao Chunliang Zhang Jingbo Zhu
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.