让大模型超越逐句翻译，实现整篇语境化重写。
Can an Old Dog Be Taught New Tricks? Taking LLMs Beyond Sentence Level Translation
- 用检索增强生成技术整合文档级上下文
- 在三个项目中实现显著语篇重构效果
- 适合需要自然语言风格适配的专业译者
自动翻译系统（从CAT工具到MT）普遍采用逐句翻译模式。本文探讨通过全文档、语料库驱动的方式，将大语言模型（LLM）从这一范式中解放出来。我们提出PAT（Pragmatic Auto-Translator），一个基于RAG的系统，结合用户配置规范与真实长文本语料库（美式英语与拉美西班牙语），将检索到的段落、章节及文档级示例传递给LLM，生成整体连贯的目标语言译文。目标是为专业校对提供可直接使用的译稿，使译文在语篇结构、修辞风格和语用规范上适配西班牙语语境。我们在三个项目中评估了六组关于生成式AI的论文翻译，使用定制化的MQM评估体系，由两名训练过的评估员从美式英语翻译至拉美及墨西哥西班牙语。结果表明，简单提示无法产生有意义的重构；而引入规范与语料库支持的翻译虽有时实现显著重构，但效果不稳定。研究发现，大模型确实可被引导脱离逐句翻译，向语境化重写演进，但仍需优化重构有效性。本文讨论了自动翻译系统设计、语料构建与质量评估方法的相关问题。
原文摘要 · Abstract (English)
Automatic translation systems, from CAT tools to MT, overwhelmingly treat translation as a sentence-by-sentence act. This paper asks whether LLMs can be moved beyond that paradigm through whole-document, corpus-informed translation. We present PAT (Pragmatic Auto-Translator), a RAG-based system that pairs user-configured specifications with context from a comparable corpus of authentic longform texts in U.S. English and Latin American Spanish, passing retrieved paragraph-, section-, and document-level examples to an LLM for whole-document generation. The goal is draft translation for professional verification: target texts reformulated to fit their Spanish-language context, where discourse organization, rhetorical style, and pragmatic norms differ meaningfully from English. We evaluated six automatic translations of essays on generative AI across three projects using a customized MQM typology, assessed by two trained evaluators working from U.S. English into LATAM and Mexican Spanish. Results show that a limited prompt produced no meaningful reformulation, and specifications and corpus-informed translations at times showed substantial reformulation, though not always to effect. We find that LLMs can be moved toward reformulation and away from the sentence-by-sentence paradigm, though more work is needed to improve the effectiveness of those reformulations. In this paper, we discuss considerations related to automatic translation system design, corpus construction, and translation quality evaluation methodology and results.
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