Conceptual Metaphor Theory as a Prompting Paradigm for Large Language Models
This addresses the challenge of improving structured and human-like reasoning in LLMs for complex tasks, though it appears incremental as it builds on existing prompting methods.
The paper tackles the problem of enhancing large language models' reasoning in complex tasks by introducing Conceptual Metaphor Theory as a prompting framework, resulting in significant improvements in accuracy, clarity, and metaphorical coherence across benchmark tasks.
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.