Leveraging GPT-4 for Automatic Translation Post-Editing
Authors: name: Vikas Raunak, name: Amr Sharaf, name: Hany Hassan Awadallah, name: Arul Menezes

While Neural Machine Translation (NMT) represents the leading approach to
Machine Translation (MT), the outputs of NMT models still require translation
post-editing to rectify errors and enhance quality, particularly under critical
settings. In this work, we formalize the task of translation post-editing with
Large Language Models (LLMs) and explore the use of GPT-4 to automatically
post-edit NMT outputs across several language pairs. Our results demonstrate
that GPT-4 is adept at translation post-editing and produces meaningful edits
even when the target language is not English. Notably, we achieve
state-of-the-art performance on WMT-22 English-Chinese, English-German,
Chinese-English and German-English language pairs using GPT-4 based
post-editing, as evaluated by state-of-the-art MT quality metrics.