用大模型生成错误提示和修正建议，提升译后编辑体验
Smarter edits? Post-editing with error highlights and translation suggestions
- 基于自动译后编辑的LLM错误标注与建议
- 比传统质检标注更受译者欢迎，提升使用体验
- 虽未提高效率或质量，但改善了编辑感受
随着机器翻译质量提升，增强型译后编辑功能（如基于质量评估的错误高亮）日益受到关注，但其实际效果证据有限。本文研究基于大语言模型生成的错误高亮与修正建议在自动译后编辑（APE）中的应用。通过专业译者（英-荷语）进行实验，对比使用APE错误高亮与建议、传统译后编辑（PE）及基于质检的高亮三种条件下的生产力、质量与用户体验。结果表明，各条件均未显著提升生产力或翻译质量，但相比质检生成的高亮，APE生成的提示更受好评，且修正建议显著改善了整体使用体验。
原文摘要 · Abstract (English)
As MT quality increases, interest in enhanced post-editing features such as QE-derived error highlights is growing, yet evidence for their usefulness remains limited. In this work, we explore the usefulness of LLM-derived error highlights and correction suggestions based on automatic post-editing (APE). We conduct a study where professional translators (En-Nl) post-edit translations using APE error highlights and correction suggestions and compare productivity, quality and user experience to regular PE and PE with QE-derived highlights. While no condition yielded productivity or quality gains compared to regular PE, APE highlights were better received than QE-derived highlights, and correction suggestions improved overall user experience.
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