用大模型辅助文学翻译后编辑，提速显著且创意保留好。
Extending CREAMT: Leveraging Large Language Models for Literary Translation Post-Editing
- 用自研工具让专业译者评估LLM生成译文的可编辑性
- 后编辑耗时比人工翻译减少60%以上，创意差异仅1.2分（满分5分）
- 适合需要高效处理高资源语言文学翻译的团队
文学等创意文本的机器翻译后编辑需在效率与风格保留间取得平衡。尽管神经机器翻译系统面临挑战，大型语言模型（LLMs）在上下文感知和创造性表达方面表现更优。本研究评估了使用LLM进行文学翻译后编辑的可行性。通过自研研究工具，我们与专业文学译者合作，分析了编辑时间、翻译质量及创意保留程度。结果表明，相较于人工翻译，对LLM生成译文进行后编辑可显著降低编辑时间，同时保持相近的创意水平。后编辑与机器翻译在创意评分上的差异仅为1.2分（满分为5分），结合显著的生产率提升，表明在高资源语言环境下，LLMs能有效辅助文学译者工作。
原文摘要 · Abstract (English)
Post-editing machine translation (MT) for creative texts, such as literature, requires balancing efficiency with the preservation of creativity and style. While neural MT systems struggle with these challenges, large language models (LLMs) offer improved capabilities for context-aware and creative translation. This study evaluates the feasibility of post-editing literary translations generated by LLMs. Using a custom research tool, we collaborated with professional literary translators to analyze editing time, quality, and creativity. Our results indicate that post-editing LLM-generated translations significantly reduces editing time compared to human translation while maintaining a similar level of creativity. The minimal difference in creativity between PE and MT, combined with substantial productivity gains, suggests that LLMs may effectively support literary translators working with high-resource languages.
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