大型语言模型促使工程作为一种体现认知语言表达的方法:特朗普话语中的政治隐喻的案例研究
Haohan Meng1, Xiaoyu Li1, Jinhua Sun1
1College of International Studies, National University of Defense Technology, Nanjing, China.
Frontiers in psychology
|July 10, 2025
概括
这项研究使用先进的人工智能来分析唐纳德·特朗普演讲中的政治隐喻,揭示了概念隐喻的战略性使用,以与观众建立联系. 大型语言模型 (LLM) 为政治语言学中的隐喻分析提供了新的工具.
科学领域:
- 认知语言学 认知语言学
- 政治语言学政治语言学
- 自然语言处理 (NLP) 是一种自然语言处理.
背景情况:
- 体化认知语言学认为语言是由社会和经济因素塑造的,隐喻是关键的认知结构.
- 政治隐喻显著影响公众的意识形态和情感反应,如在政治话语中所见.
- 大型语言模型 (LLM) 的进步正在改变比喻识别的传统方法.
研究的目的:
- 通过LLMs识别和分析唐纳德特朗普演讲中的政治隐喻.
- 探索在政治传播中概念隐喻的战略使用.
- 评估LLMs在分析政治话语中的有效性.
主要方法:
- 批判性隐喻分析 (CMA) 框架.
- 基于思维链的快速工程 (PE) 技术.
- 在政治演讲中使用ChatGPT-4.0进行文本解析和隐喻识别.
主要成果:
- 唐纳德·特朗普从运动,健康和力量等源域中战略性地使用隐喻.
- 这些比喻被用来创造与观众的情感共.
- 法律学士在分析政治话语方面表现出优势,但在语义差异化方面面临挑战.
结论:
- 法律法学,特别是当与快速工程结合时,是分析政治隐喻的强大工具.
- 需要进一步的研究来优化NLP和政治语言学的LLMs,包括跨文化应用.
- 这项研究强调了理解政治沟通中的概念隐喻的重要性.
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