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Science is Exploration: Computational Frontiers for Conceptual Metaphor Theory

Rebecca M. M. Hicke, Ross Deans Kristensen-McLachlan

TL;DR

It is demonstrated that LLMs are a promising tool for large-scale computational research on conceptual metaphors, and it is shown that LLMs are able to apply procedural guidelines designed for human annotators, displaying a surprising depth of linguistic knowledge.

Abstract

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.

Science is Exploration: Computational Frontiers for Conceptual Metaphor Theory

TL;DR

It is demonstrated that LLMs are a promising tool for large-scale computational research on conceptual metaphors, and it is shown that LLMs are able to apply procedural guidelines designed for human annotators, displaying a surprising depth of linguistic knowledge.

Abstract

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.

Paper Structure

This paper contains 6 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methods
  4. Data
  5. Results and Discussion
  6. Conclusion

Figures (5)

  • Figure 1: The metaphor identification procedure (MIP) introduced by the Pragglejaz Group in group2007mip.
  • Figure 2: The modified prompt used in all experiments. All significant (non-formatting) changes from Step 3b of the MIP procedure are highlighted in red. The sentence to be analyzed is appended in quotations to the bottom of the prompt when querying models.
  • Figure 3: Confusion matrices for results on the TroFi dataset for each model. The rows represent the true values for each sample, and the columns represent the model labels.
  • Figure 4: The percentage of samples from Lakoff and Johnson where all metaphors and basic meanings are correctly identified.
  • Figure 5: The percentage of samples from Lakoff and Johnson in which additional metaphors have been labeled. Smaller bars are included which represent the percentage of additional words for which the correct basic meaning is provided (upper bar) and which are plausible metaphors (lower bar).