LLM翻译有独特情绪特征，后编辑能使其更接近人工译文。
Emotion Profiling in LLM-Based Literary Translation: Systematic Shifts Across MT and Post-Editing
- 用词典和多语言模型分析翻译中的情绪特征。
- 不同机器翻译系统产生显著不同的情绪指纹。
- 后编辑可有效改善情绪表达，更贴近人类译者风格。
本文研究大语言模型（LLM）翻译是否具有可识别的情绪特征，以及后编辑如何重塑这些特征以趋近人类译文的自然性。通过对比玛格丽特·阿特伍德《使女的故事》的LLM翻译、经人工后编辑的版本与真人译本，并以当代意大利科幻小说语料库为基准，采用基于词典和多语言建模的方法，对各系统间的情感差异进行细粒度分析。结果表明，机器翻译系统在不同译文中引入了模型特有的、统计上显著的情绪特征，导致作者原作情绪风格的有限保留。
原文摘要 · Abstract (English)
This paper investigates whether LLM translations exhibit identifiable emotional profiles and how post-editing reshapes them toward human-like norms. We compare LLM translations of Margaret Atwood's Oryx and Crake with their post-edited versions and a human translation, using a large-scale corpus of contemporary Italian science-fiction as a baseline. We examine emotion through lexicon-based and multilingual modeling, conducting a fine-grained analysis of emotional variation across systems. We find that MT systems introduce model-specific and statistically significant emotional fingerprints across translations, leading to a limited preservation of an author's voice.
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