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.

翻译：随着机器翻译质量的提升，人们对基于质量估计的错误高亮等增强型译后编辑功能的兴趣日益增长，但支持其有效性的证据仍然有限。本研究探索了基于自动译后编辑（APE）的大语言模型错误高亮与修正建议的实际应用价值。我们开展了一项实验，邀请专业译者（英-荷）使用自动译后编辑提供的错误高亮与修正建议进行译后编辑，并将其在生产力、质量及用户体验方面与常规译后编辑及基于质量估计高亮的译后编辑进行对比。结果表明，虽无任何条件在生产力或质量上优于常规译后编辑，但自动译后编辑高亮比质量估计高亮获得更积极的评价，且修正建议显著提升了整体用户体验。