用两次中间翻译提升文档级翻译质量，更关注难句。
Two Intermediate Translations Are Better Than One: Fine-tuning LLMs for Document-level Translation Refinement
- 用两句间和文档间翻译做双中间步骤，融合两者优势。
- 在10个任务上提升翻译质量，尤其改善困难句子。
- 根据翻译难度动态加权，让模型重点学难例。
近期研究显示大型语言模型可通过自修正提升翻译质量。本文将这一思路扩展至文档级翻译，聚焦于文档到文档（Doc2Doc）的翻译优化。由于句对句（Sent2Sent）与文档对文档（Doc2Doc）翻译处理不同层面，我们提出通过两次中间翻译来微调大模型，结合两者优势。此外，考虑到中间翻译质量不一，引入具有质量感知的增强微调方法，对简单翻译赋予较低权重，对困难翻译赋予更高权重，使模型更聚焦于挑战性任务。在LLaMA-3-8B-Instruct与Mistral-Nemo-Instruct模型上，跨10个翻译任务的实验验证了该方法的有效性。
原文摘要 · Abstract (English)
Recent research has shown that large language models (LLMs) can enhance translation quality through self-refinement. In this paper, we build on this idea by extending the refinement from sentence-level to document-level translation, specifically focusing on document-to-document (Doc2Doc) translation refinement. Since sentence-to-sentence (Sent2Sent) and Doc2Doc translation address different aspects of the translation process, we propose fine-tuning LLMs for translation refinement using two intermediate translations, combining the strengths of both Sent2Sent and Doc2Doc. Additionally, recognizing that the quality of intermediate translations varies, we introduce an enhanced fine-tuning method with quality awareness that assigns lower weights to easier translations and higher weights to more difficult ones, enabling the model to focus on challenging translation cases. Experimental results across ten translation tasks with LLaMA-3-8B-Instruct and Mistral-Nemo-Instruct demonstrate the effectiveness of our approach.
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