"VoT" Gives LLMs Spacial Reasoning AND Open-Source "Large Action Model"

The research paper discusses using the “visualization of thought” (VoT) technique to enhance large language models (LLMs) with spatial reasoning abilities, improving performance in tasks requiring spatial awareness. Microsoft Research’s open-source project, Pi Win Assistant, showcases the practical application of VoT in creating a large action model for controlling human interfaces through natural language commands, demonstrating the benefits of spatial reasoning enhancements in LLMs for diverse applications.

A research paper titled “Visualization of Thought Elicits Spatial Reasoning and Large Language Models” discusses enhancing large language models (LLMs) with spatial reasoning abilities. Spatial reasoning involves visualizing relationships in a 3D or 2D environment, a skill historically challenging for LLMs. The paper proposes the “visualization of thought” (VoT) prompting technique to elicit spatial reasoning in LLMs. VoT prompts LLMs to visualize reasoning steps in an interleaved manner, leading to significant performance improvements in tasks requiring spatial awareness such as navigation and tiling.

Microsoft Research has released an open-source project called Pi Win Assistant, applying VoT techniques to create a large action model that controls human interfaces using natural language commands. The project demonstrates how the assistant can navigate Windows environments and perform tasks like opening applications and interacting with web browsers, showcasing the practical application of spatial reasoning in LLMs. The assistant visually represents each step it takes, allowing users to track its decision-making process.

The VoT prompting technique involves generating mental images and visual state tracking in LLMs. By visually representing each reasoning step, LLMs can improve their performance in tasks requiring spatial awareness, such as natural language navigation, visual navigation, and visual tiling. The paper compares various prompting techniques, showing that VoT consistently outperforms other methods in enhancing LLM spatial reasoning capabilities across different tasks.

Pi Win Assistant exemplifies the successful implementation of VoT techniques in a real-world setting, enabling users to control user interfaces through natural language commands. The assistant’s ability to follow multi-step instructions, such as making posts on Twitter or navigating web browsers, showcases the effectiveness of VoT prompting in improving LLM performance. The project demonstrates how advanced LLMs can benefit from spatial reasoning enhancements for diverse applications.

Overall, the research paper and Pi Win Assistant project highlight the significance of spatial reasoning in advancing LLM capabilities for human-computer interactions. By incorporating VoT prompting techniques, LLMs can better understand and visualize spatial relationships, enabling them to perform complex tasks with improved accuracy and efficiency. The open-source nature of the project encourages further exploration and development of spatial reasoning in LLMs, potentially shaping the future of artificial narrow intelligence and enhancing user experiences in various domains.