The Power of Bullet Lists: A Simple Yet Effective Prompting Approach to Enhancing Spatial Reasoning in Large Language Models

Ikhyun Cho, Changyeon Park, Julia Hockenmaier


Abstract
While large language models (LLMs) are dominating the field of natural language processing, it remains an open question how well these models can perform spatial reasoning. Contrary to recent studies suggesting that LLMs struggle with spatial reasoning tasks, we demonstrate in this paper that a novel prompting technique, termed Patient Visualization of Thought (Patient-VoT), can boost LLMs’ spatial reasoning abilities. The core idea behind Patient-VoT is to explicitly integrate *bullet lists, coordinates, and visualizations* into the reasoning process. By applying Patient-VoT, we achieve a significant boost in spatial reasoning performance compared to prior prompting techniques. We also show that integrating bullet lists into reasoning is effective in planning tasks, highlighting its general effectiveness across different applications.
Anthology ID:
2025.findings-naacl.166
Volume:
Findings of the Association for Computational Linguistics: NAACL 2025
Month:
April
Year:
2025
Address:
Albuquerque, New Mexico
Editors:
Luis Chiruzzo, Alan Ritter, Lu Wang
Venue:
Findings
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Publisher:
Association for Computational Linguistics
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Pages:
3047–3057
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URL:
https://preview.aclanthology.org/landing_page/2025.findings-naacl.166/
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Cite (ACL):
Ikhyun Cho, Changyeon Park, and Julia Hockenmaier. 2025. The Power of Bullet Lists: A Simple Yet Effective Prompting Approach to Enhancing Spatial Reasoning in Large Language Models. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 3047–3057, Albuquerque, New Mexico. Association for Computational Linguistics.
Cite (Informal):
The Power of Bullet Lists: A Simple Yet Effective Prompting Approach to Enhancing Spatial Reasoning in Large Language Models (Cho et al., Findings 2025)
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https://preview.aclanthology.org/landing_page/2025.findings-naacl.166.pdf