用提示工程让大模型读懂中医隐喻，翻译更贴近原意。
Conveying Imagistic Thinking in Traditional Chinese Medicine Translation: A Prompt Engineering and LLM-Based Evaluation Framework
- 通过提示词引导大模型识别中医文本中的隐喻和转喻。
- 改进后的翻译在五项认知维度上表现最佳，跨模型一致性高。
- 适合对中医哲学理解有需求的译者与研究者参考。
中医理论基于意象思维，其医理、诊断与治疗逻辑多借助隐喻与转喻构建。然而现有英译多采用字面直译，导致目标读者难以重构深层概念网络并应用于临床。本研究采用人机协同框架，选取《黄帝内经》中四段基础理论文本，通过提示词引导DeepSeek V3.1识别源语文本中的隐喻与转喻，并实现理论传递。在评估阶段，利用ChatGPT 5 Pro与Gemini 2.5 Pro模拟三类真实读者，对人工翻译、基线模型翻译及提示调整后翻译进行评分，涵盖五个认知维度，并辅以结构化访谈与解释现象学分析（IPA）。结果显示，提示调整后的生成翻译在所有维度上表现最优，且跨模型与跨角色评分一致性高。访谈揭示了人机翻译差异、隐喻转喻传递的有效策略以及读者的认知偏好。本研究为《黄帝内经》等古代密集型概念文本的翻译提供了认知导向、高效且可复现的人机协同路径。
原文摘要 · Abstract (English)
Traditional Chinese Medicine (TCM) theory is built on imagistic thinking, in which medical principles and diagnostic and therapeutic logic are structured through metaphor and metonymy. However, existing English translations largely rely on literal rendering, making it difficult for target-language readers to reconstruct the underlying conceptual networks and apply them in clinical practice. This study adopted a human-in-the-loop (HITL) framework and selected four passages from the medical canon Huangdi Neijing that are fundamental in theory. Through prompt-based cognitive scaffolding, DeepSeek V3.1 was guided to identify metaphor and metonymy in the source text and convey the theory in translation. In the evaluation stage, ChatGPT 5 Pro and Gemini 2.5 Pro were instructed by prompts to simulate three types of real-world readers. Human translations, baseline model translations, and prompt-adjusted translations were scored by the simulated readers across five cognitive dimensions, followed by structured interviews and Interpretative Phenomenological Analysis (IPA). Results show that the prompt-adjusted LLM translations perform best across all five dimensions, with high cross-model and cross-role consistency. The interview themes reveal differences between human and machine translation, effective strategies for metaphor and metonymy transfer, and readers' cognitive preferences. This study provides a cognitive, efficient, and replicable HITL methodological pathway for the translation of ancient, concept-dense texts such as TCM.
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