Despite the strong language understanding exhibited by large language models (LLMs), their spatial reasoning capabilities are still an area of exploration. This paper introduces a method known as Visualization-of-Thought (VoT) prompting, which leverages the concept of the human ‘Mind’s Eye’ to bolster the LLMs’ ability to conduct spatial reasoning.

VoT encourages models to ‘visualize’ their thinking process, thus guiding them through complex multi-hop spatial reasoning tasks such as navigation in natural language, visual navigation, and tiled pattern creation in simulated environments.

Highlights of the paper include:

• The VoT approach significantly elevates LLMs’ abilities to perform spatial reasoning tasks.
• It outperforms existing multimodal large language models in these spatial tasks.
• VoT’s process emulates human cognitive abilities to facilitate spatial reasoning.

Delve into the full study here.

The concept of VoT has groundbreaking implications for the development of AI agents with enhanced cognitive functions similar to human imagination. By introducing a way for LLMs to ‘envision’ scenarios for better reasoning, this research opens a new frontier in AI, where models could potentially solve more complex, abstract problems in a human-like manner.