AI提示词工程与译者行为研究以中国典籍英译中的人机协同翻译为例

    AI Prompt Engineering and Translator BehaviorEvidence from Human-Machine Collaboration in Chinese Classics English Translation

    • 摘要: 随着生成式人工智能在翻译领域的广泛应用,传统译者行为理论所依赖的互动模式正面临新的挑战。文章以中国典籍英译为例,提出AI时代人机协作翻译工作新的模式及其理论基础。译者通过提示词的设计、调整与优化来引导人工智能的生成过程,这不仅是技术操作,更是一种具有理论意涵的新型译者行为。文章在三个维度上展开论证:其一,从框架语义学视角揭示译者如何通过提示词将文化认知框架外化为 AI 可理解的指令;其二,从关联理论视角分析译者如何在认知效果与处理努力之间寻求最优平衡;其三,从现象学视角阐释提示词如何成为意义生成的场域,使译者成为意义设计者。研究表明,提示词的设计、实施与反思构成了 AI 时代译者行为的完整链条,译者由此转变为“翻译设计者”,通过元语言能力在人类认知与机器智能之间搭建桥梁。这一模式转向不仅回应了技术变革对翻译职业的挑战,也为理解和发展 AI 时代的译者专业能力提供了新的理论框架。

       

      Abstract: With the growing application of generative artificial intelligence in translation, the traditional “translator–text” interaction model underpinning translator behavior theory faces unprecedented challenges. Using English translations of Chinese classics as illustrative cases, this article advances the thesis of a paradigm shift in translator behavior: translators now design, adjust, and optimize prompts to guide AI’s generative processes. This is not merely a technical operation but a new form of translator behavior with theoretical significance. The argument is developed across three dimensions: first, from the perspective of Frame Semantics, prompts externalize cultural cognitive frames into machine-readable instructions; second, from the perspective of Relevance Theory, prompts enable translators to optimize the balance between cognitive effects and processing effort; third, from a phenomenological perspective, prompts constitute the generative field of meaning, positioning translators as designers of emergent meanings. The study demonstrates that the design, implementation, and reflection of prompts form a complete behavioral chain in the AI era, redefining the translator as a “translation architect” who bridges human cognition and machine intelligence through metalinguistic agency. This paradigm shift not only responds to the challenges posed by technological transformation to the translation profession, but also offers a new theoretical framework for understanding and advancing translator competence in the age of AI.

       

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