Visualization-of-Thought in Large Language Models

Exploring the spatial reasoning capabilities of LLMs, researchers developed Visualization-of-Thought (VoT) to enhance this skill. Detailed in the paper Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models, VoT guides LLMs to visualize reasoning steps, mimicking human cognition and imagination. This approach has outperformed existing multimodal LLMs in spatial reasoning tasks.

• VoT prompts are designed to trigger spatial reasoning by visual representation.
• LLMs with VoT surpassed multimodal LLMs in navigation and visual tasks.
• The technique draws inspiration from human cognitive abilities.
• It suggests a potential application in enhancing MLLMs with mental imagery.

The significance of this paper is twofold. First, it presents a novel method that pushes the boundaries of LLMs’ abilities closer to a human-like understanding of space and environment. Second, it touches upon an often-neglected aspect of LLM capabilities, paving the way for more immersive and multisensory AI applications in the future.