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
- 适合研究中医翻译、认知语言学或AI辅助古籍的学者
中医理论基于意象思维，其医理、诊断与治疗逻辑多依赖隐喻与转喻构建。然而现有英文翻译多采用字面直译，导致目标读者难以重构深层概念网络，影响临床应用。本研究采用人机协同框架，选取《黄帝内经》中四段核心理论文本，利用提示词进行认知支架设计，指导DeepSeek V3.1识别源语文本中的隐喻与转喻，并实现理论传递。评估阶段，以ChatGPT 5 Pro与Gemini 2.5 Pro模拟三类真实读者，对人类译文、基线模型译文及提示调整后译文，在五个认知维度上进行评分，并辅以结构化访谈与诠释现象学分析。结果表明，提示调整后的大模型译文在所有维度表现最优，跨模型与跨角色一致性高。访谈揭示了人机翻译差异、隐喻转喻传递的有效策略及读者认知偏好。本研究为《黄帝内经》等概念密集型古籍翻译提供了可复制、高效的认知型人机协同路径。
原文摘要 · Abstract (English)
Traditional Chinese Medicine 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 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. 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 translation of ancient, concept-dense texts like TCM.
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