title: Leveraging GPT-4 for Automatic Translation Post-Editing
summary: 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.
paper_authors._id: 646f0a74041e48e1c47062da
paper_authors.name: Vikas Raunak
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-05-26T08:11:35.877Z
paper_authors._id: 5f06175e5d08220171a0ae32
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1652484966062-5f06175e5d08220171a0ae32.jpeg
paper_authors.fullname: Vikas Raunak
paper_authors.type: user
paper_authors.user: vyraun
paper_authors._id: 646f0a74041e48e1c47062db
paper_authors.name: Amr Sharaf
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-05-26T08:12:00.351Z
paper_authors._id: 619ffce1ea7f7717b42344c6
paper_authors.avatarUrl: /avatars/95bbb992cadfc8c3523ec2850d6e1e65.svg
paper_authors.fullname: Amr Sharaf
paper_authors.type: user
paper_authors.user: amrsharaf
paper_authors._id: 646f0a74041e48e1c47062dc
paper_authors.name: Hany Hassan Awadallah
paper_authors._id: 646f0a74041e48e1c47062dd
paper_authors.name: Arul Menezes
paper_title: Leveraging GPT-4 for Automatic Translation Post-Editing
paper_summary: 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.
paper_ai_summary: GPT-4 demonstrates superior performance in automatic post-editing of NMT outputs across multiple language pairs, achieving state-of-the-art results.
paper_ai_keywords: Neural Machine Translation Large Language Models GPT-4 translation post-editing WMT-22