Science is Exploration: Computational Frontiers for Conceptual Metaphor Theory
This work addresses the challenge of automating metaphor analysis for researchers in cognitive science and computational linguistics, though it appears incremental in applying existing LLMs to a new task.
The paper tackled the problem of whether Large Language Models (LLMs) can identify and explain conceptual metaphors in natural language, demonstrating that LLMs are a promising tool for large-scale computational research on this topic.
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.