Augmenting Large Language Model Translators via Translation Memories
Yongyu Mu, Abudurexiti Reheman, Zhiquan Cao, Yuchun Fan, Bei Li, Yinqiao Li, Tong Xiao, Chunliang Zhang
Abstract
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.
BibTeX
@inproceedings{mu-etal-2023-augmenting,
title = "Augmenting Large Language Model Translators via Translation Memories",
author = "Mu, Yongyu and
Reheman, Abudurexiti and
Cao, Zhiquan and
Fan, Yuchun and
Li, Bei and
Li, Yinqiao and
Xiao, Tong and
Zhang, Chunliang and
Zhu, Jingbo",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.653/",
doi = "10.18653/v1/2023.findings-acl.653",
pages = "10287--10299"
}