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

Introduction to Open-Source Large Action Model

In this section, the speaker introduces an open-source large action model for the Windows environment released by Microsoft, similar to how the Rabbit R1 controls applications in Android through natural language.

Open-Source Large Action Model

• Microsoft released a research paper outlining their achievement and an open-source project available for immediate use.

• The paper discusses providing large language models with spatial reasoning abilities crucial for visualization of relationships between objects.

• Spatial reasoning example: Walking from the North Pole prompts mental imagery without language involvement.

Importance of Spatial Reasoning in Language Models

This part emphasizes the significance of spatial reasoning in large language models and challenges the notion that it is unattainable.

Significance of Spatial Reasoning

• Large language models excel but lack spatial reasoning crucial for human cognition.

• "Visualization of Thought" technique can enhance large action models by integrating spatial awareness into user interfaces.

Enhancing Performance Through Prompting Techniques

Discusses various prompting techniques to improve performance in large language models, focusing on visualization of thought.

Prompting Techniques

• Chain of Thought prompting enhances model performance significantly.

• Visualization of Thought prompts LLMS to visualize reasoning steps, leading to substantial performance improvements.

Visualization of Thought Prompts for Spatial Awareness

Explores how Visualization of Thought (VOT) prompting enhances spatial awareness in large language models through visual-spatial sketch pads.

VOT Prompts for Spatial Awareness

• VOT augments LLMS with visual-spatial sketch pads, improving performance on tasks requiring spatial awareness.

Spatial Reasoning Challenges and Solutions

This section delves into spatial reasoning challenges faced by large language models and introduces innovative solutions to enhance their spatial awareness capabilities.

Spatial Reasoning Definition and Importance

• : Spatial reasoning involves comprehending and reasoning about the spatial relationships, movements, and interactions among objects. It is crucial for navigation, robotics, and autonomous driving.

Visual Navigation Tasks

• : Visual navigation tasks require large language models to navigate a synthetic 2D grid world using visual cues. The model must generate instructions to move in four directions (left, right, up, down) while avoiding obstacles.

Spatial Reasoning Tests: Visual Tiling

• : Visual tiling tests the ability of large language models to comprehend, organize, and reason with shapes in a confined area. It extends the classic spatial reasoning challenge of polyomino tiling.

VOT Prompting Technique

• : VOT prompting introduces a new paradigm for spatial reasoning by visualizing the state after each reasoning step. This technique aims to generate reasoning traces and visualizations in an interleaved manner.

Performance Analysis with Different Prompting Techniques

Partial Tracking Rate and Next Step Prediction

The discussion focuses on the partial tracking rate and the performance of different models in Next Step prediction tasks.

Partial Tracking Rate

• Partial tracking rate indicates at least one step had visualization, showing similar results across models.

• Limitations exist for mental images and visual state tracking due to reliance on advanced llms, potentially causing performance issues in less advanced language models or challenging tasks.

Pi Win Assistant Project Overview

Introduction to the Pi Win Assistant project, an open-source large action model generalist artificial narrow intelligence controlling human interfaces through natural language.

Pi Win Assistant Features

• Pi Win Assistant is described as the first open-source large action model generalist artificial narrow intelligence controlling human interfaces using natural language.

• The project references a paper demonstrating its ability to control a Windows environment with a character interface for various tasks.

Examples of Pi Win Assistant in Action

Demonstrations of Pi Win Assistant's capabilities through examples in a Windows environment.

Examples of Task Execution

• Instructing the assistant to open Firefox, navigate to YouTube, and perform specific actions without visual context.

• Guiding the assistant to search for "Rick Roll" on YouTube step by step without prior training on screen elements.

Advanced Task Execution and Planning

Further exploration of Pi Win Assistant's abilities in executing complex tasks with detailed planning steps.

Detailed Task Execution

• Successfully completing tasks like muting a video or making a new post on Twitter by following precise instructions without prior knowledge of screen elements.

• Planning upfront by providing detailed steps such as clicking on browser address bars or entering specific websites before execution.

Proven Working Cases and Implementation

Highlighting successful cases and implementations of Pi Win Assistant's capabilities in various scenarios.

Successful Implementations

• Demonstrating successful task completions like opening new tabs, sending jokes about engineers, and creating social media posts through natural language commands.