用大模型识别语言中的概念隐喻，发现其能像人一样理解隐喻机制。
Science is Exploration: Computational Frontiers for Conceptual Metaphor Theory
- 基于隐喻标注规范设计新提示方法，让大模型识别概念隐喻
- 模型在隐喻识别任务中表现接近人类标注者，具备深层语言理解能力
- 适合研究认知科学、语言学与AI交叉领域的学者参考
隐喻无处不在，从诗歌到学术文本均广泛存在。认知语言学研究表明，概念隐喻是通过一个经验领域系统性地表达另一领域的重要认知机制，而非修辞点缀。本文探讨大型语言模型（LLMs）是否能准确识别并解释自然语言中的概念隐喻。通过基于隐喻标注指南的新型提示技术，我们证明了LLMs在大规模计算研究中具有巨大潜力。此外，模型能有效遵循为人类标注者设计的操作流程，展现出令人意外的语言知识深度。
原文摘要 · Abstract (English)
Metaphors are everywhere. They appear extensively across all domains of natural language, from the most sophisticated poetry to seemingly dry academic prose. A significant body of research in the cognitive science of language argues for the existence of conceptual metaphors, the systematic structuring of one domain of experience in the language of another. Conceptual metaphors are not simply rhetorical flourishes but are crucial evidence of the role of analogical reasoning in human cognition. In this paper, we ask whether Large Language Models (LLMs) can accurately identify and explain the presence of such conceptual metaphors in natural language data. Using a novel prompting technique based on metaphor annotation guidelines, we demonstrate that LLMs are a promising tool for large-scale computational research on conceptual metaphors. Further, we show that LLMs are able to apply procedural guidelines designed for human annotators, displaying a surprising depth of linguistic knowledge.
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