Iterative Translation Refinement with Large Language Models
Pinzhen Chen Zhicheng Guo Barry Haddow Kenneth Heafield
Large language models have shown surprising performances in understanding
instructions and performing natural language tasks. In this paper, we propose
iterative translation refinement to leverage the power of large language models
for more natural translation and post-editing. We show that by simply involving
a large language model in an iterative process, the output quality improves
beyond mere translation. Extensive test scenarios with GPT-3.5 reveal that
although iterations reduce string-based metric scores, neural metrics indicate
comparable if not improved translation quality. Further, human evaluations
demonstrate that our method effectively reduces translationese compared to
initial GPT translations and even human references, especially for into-English
directions. Ablation studies underscore the importance of anchoring the
refinement process to the source input and a reasonable initial translation.