Cognitive Linguistic-Inspired Prompt Templates for Enhanced Model Interpretability

Meng et al. (2025) showed how cognitive linguistics and metaphor analysis can be leveraged for nuanced LLM political discourse analysis. Taking this further, why not systematically create prompt templates based on cognitive science principles—like frame semantics, mental models, or metaphorical mapping—to “probe” and steer the model’s reasoning pathways? Unlike generic prompt patterns (Amatriain, 2024; Leung & Shen, 2024), this approach would explicitly encode cognitive frameworks into prompts, then empirically test if and how model responses align with human conceptual structures. The research could involve both interpretability studies (do LLMs reveal internal metaphorical thinking?) and applications (can such prompts reduce hallucinations or bias by anchoring reasoning in human-like concepts?). This synthesis between cognitive science and prompt engineering offers a pathway to more transparent and cognitively aligned AI.

References:

1. Prompt Design and Engineering: Introduction and Advanced Methods. Xavier Amatriain (2024). arXiv.org.
2. Prompt Engineering for Curriculum Design. Jonathan Leung, Zhiqi Shen (2024). International Conference on Emerging Technologies.
3. Large language models prompt engineering as a method for embodied cognitive linguistic representation: a case study of political metaphors in Trump’s discourse. Haohan Meng, Xiaoyu Li, Jinhua Sun (2025). Frontiers in Psychology.