# Conceptual Metaphor Theory as a Prompting Paradigm for Large Language Models

arXiv: https://arxiv.org/abs/2502.01901 · HTML: https://mlsanity.com/paper/2502.01901/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 4 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [Large Language Models as Optimizers](https://mlsanity.com/paper/2309.03409/) (2023)
- [Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution](https://mlsanity.com/paper/2309.16797/) (2023)
- [Psychologically-informed chain-of-thought prompts for metaphor understanding in large language models](https://mlsanity.com/paper/2209.08141/) (2022)
- [MiQA: A Benchmark for Inference on Metaphorical Questions](https://mlsanity.com/paper/2210.07993/) (2022)
- [Unlocking Structured Thinking in Language Models with Cognitive Prompting](https://mlsanity.com/paper/2410.02953/) (2024)

## Built on by (most cited first, 3 of 3)

- [A Survey of Context Engineering for Large Language Models](https://mlsanity.com/paper/2507.13334/) (2025)
- [The Mind's Eye: A Multi-Faceted Reward Framework for Guiding Visual Metaphor Generation](https://mlsanity.com/paper/2508.18569/) (2025)
- [Umwelt Engineering: Designing the Cognitive Worlds of Linguistic Agents](https://mlsanity.com/paper/2603.27626/) (2026)

## Abstract (arXiv)

We introduce Conceptual Metaphor Theory (CMT) as a framework for enhancing large language models (LLMs) through cognitive prompting in complex reasoning tasks. CMT leverages metaphorical mappings to structure abstract reasoning, improving models' ability to process and explain intricate concepts. By incorporating CMT-based prompts, we guide LLMs toward more structured and human-like reasoning patterns. To evaluate this approach, we compare four native models (Llama3.2, Phi3, Gemma2, and Mistral) against their CMT-augmented counterparts on benchmark tasks spanning domain-specific reasoning, creative insight, and metaphor interpretation. Responses were automatically evaluated using the Llama3.3 70B model. Experimental results indicate that CMT prompting significantly enhances reasoning accuracy, clarity, and metaphorical coherence, outperforming baseline models across all evaluated tasks.