  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 under critical settings. In
this work, we formalize the task of direct 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, producing meaningful and trustworthy
edits to translations that help improve its general quality as well as remove
different classes of major errors in translations. In particular, human
evaluations on assessing edit trustworthiness show that GPT-4 exhibits a large
improvement over the prior state-of-the-art LLM. Notably, we improve upon
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. However, we
also show that GPT-4 could produce hallucinated edits, thereby urging caution
in its use as an expert translation post-editor.
