# Science is Exploration: Computational Frontiers for Conceptual Metaphor Theory

arXiv: https://arxiv.org/abs/2410.08991 · HTML: https://mlsanity.com/paper/2410.08991/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 28 references that are in the map.
- First posted on arXiv 11 Oct 2024.

## Builds on (most cited first, 2 of 2)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Metaphors in Pre-Trained Language Models: Probing and Generalization Across Datasets and Languages](https://mlsanity.com/paper/2203.14139/) (2022)

## Built on by (most cited first, 4 of 4)

- [Metaphor identification using large language models: A comparison of RAG, prompt engineering, and fine-tuning](https://mlsanity.com/paper/2509.24866/) (2025)
- [Enhancing multimodal analogical reasoning with Logic Augmented Generation](https://mlsanity.com/paper/2504.11190/) (2025)
- [Meanings are like Onions: a Layered Approach to Metaphor Processing](https://mlsanity.com/paper/2507.10354/) (2025)
- [LinguistAgent Technical Report: A Reflective Multi-Model Platform for Automated Linguistic Annotation](https://mlsanity.com/paper/2602.05493/) (2026)

## Abstract (arXiv)

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.