用术语库增强提示，让大模型翻译岩画文献更准确
AI-assisted cultural heritage dissemination: Comparing NMT and glossary-augmented LLM translation in rock art documents
- 用术语对检索增强大模型提示，提升专业术语一致性
- 术语准确率81.4%，显著高于简单提示（69.1%）和NMT（64.4%）
- 适合资源有限的文化遗产机构快速部署高质量多语种传播
文化遗产机构日益在全球传播研究与阐释材料，但受限于预算和人力，多语言传播面临挑战。在术语密集的岩画领域，翻译质量取决于专业术语的准确与一致，微小词汇错误可能误导非专业人士并降低复用性。本文对比三种英文机器翻译方案：(1) DeepL作为强基准的NMT，(2) Gemini-Simple（基础提示的大模型），(3) Gemini-RAG（基于术语对检索的增强提示）。通过PEARMUT进行人工评估，包括多向直接评分（0–100）和基于受限MQM分类的术语审计。Gemini-RAG在术语精确匹配上表现最佳（81.4%），优于Gemini-Simple（69.1%）和DeepL（64.4%），整体质量均值达85.3，略高于Gemini-Simple（85.2），显著优于DeepL（80.3）。结果表明，若机构维护基础术语资源并采用轻量评估流程，术语增强提示是提升文化遗产翻译术语控制的低开销有效方法。
原文摘要 · Abstract (English)
Cultural heritage institutions increasingly disseminate research and interpretive materials globally, but multilingual dissemination is constrained by limited budgets and staffing. In terminology-dense domains such as rock art, translation quality depends on accurate, consistent specialised terms, and small lexical errors can mislead non-specialists and reduce reuse. We compare three English MT setups for a Spanish academic rock art text, focusing on simple, operationally feasible interventions rather than complex model-side modifications: (1) DeepL as a strong NMT baseline, (2) Gemini-Simple (LLM with a basic prompt), and (3) Gemini-RAG (the same LLM with glossary-augmented prompting via term-pair retrieval). Using PEARMUT, we conduct a human evaluation via (i) multi-way Direct Assessment (0--100) and (ii) targeted terminology auditing with a restricted MQM taxonomy. Gemini-RAG yields the highest exact-match terminology accuracy (81.4\%), versus Gemini-Simple (69.1\%) and DeepL (64.4\%), while preserving overall quality (mean DA 85.3 Gemini-RAG vs. 85.2 Gemini-Simple), outperforming DeepL (80.3). These results show that glossary-augmented prompting is a low-overhead way to improve terminology control in cultural-heritage translation if institutions maintain minimal terminology resources and lightweight evaluation procedures.
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