Conveying Imagistic Thinking in Traditional Chinese Medicine Translation: A Prompt Engineering and LLM-Based Evaluation Framework
On the map: LLM reasoning → Clinical and biomedical applications
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning. Builds on 12 of its 34 references that are in the map. First posted on arXiv 1 Dec 2025.
Builds on
12 of 34 references.
References · 12
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- 2021BARTScore: Evaluating Generated Text as Text Generationcited by 722
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- 2024Human-in-the-loop or AI-in-the-loop? Automate or Collaborate?cited by 23
- 2024Machine Translation with Large Language Models: Prompt Engineering for Persian, English, and Russian Directionscited by 12
- 2024The power of Prompts: Evaluating and Mitigating Gender Bias in MT with LLMscited by 12
- 2024CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generationcited by 11
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- 2023Human-in-the-loop Machine Translation with Large Language Modelcited by 4
- 2025From Metaphor to Mechanism: How LLMs Decode Traditional Chinese Medicine Symbolic Language for Modern Clinical Relevancecited by 3
- 2025Long-context Reference-based MT Quality Estimationcited by 2
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- 2025Conveying Imagistic Thinking in Traditional Chinese Medicine Translation: A Prompt Engineering and LLM-Based Evaluation Framework
- 2025MTCMB: A Multi-Task Benchmark Framework for Evaluating LLMs on Knowledge, Reasoning, and Safety in Traditional Chinese Medicine
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- 2026TCMClinicalReason-Bench: Can Language Models Reason from Pathogenesis to Prescription over Real-World Clinical Cases?
- 2025TCM-Eval: An Expert-Level Dynamic and Extensible Benchmark for Traditional Chinese Medicine
- 2025A benchmark dataset for evaluating Syndrome Differentiation and Treatment in large language models
- 2026LingLanMiDian: Systematic Evaluation of LLMs on TCM Knowledge and Clinical Reasoning
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