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arxiv:2409.10038

On the Diagram of Thought

Published on Sep 16
· Submitted by yifAI on Sep 17
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Abstract

We introduce Diagram of Thought (DoT), a framework that models iterative reasoning in large language models (LLMs) as the construction of a directed acyclic graph (DAG) within a single model. Unlike traditional approaches that represent reasoning as linear chains or trees, DoT organizes propositions, critiques, refinements, and verifications into a cohesive DAG structure, allowing the model to explore complex reasoning pathways while maintaining logical consistency. Each node in the diagram corresponds to a proposition that has been proposed, critiqued, refined, or verified, enabling the LLM to iteratively improve its reasoning through natural language feedback. By leveraging auto-regressive next-token prediction with role-specific tokens, DoT facilitates seamless transitions between proposing ideas and critically evaluating them, providing richer feedback than binary signals. Furthermore, we formalize the DoT framework using Topos Theory, providing a mathematical foundation that ensures logical consistency and soundness in the reasoning process. This approach enhances both the training and inference processes within a single LLM, eliminating the need for multiple models or external control mechanisms. DoT offers a conceptual framework for designing next-generation reasoning-specialized models, emphasizing training efficiency, robust reasoning capabilities, and theoretical grounding. The code is available at https://github.com/diagram-of-thought/diagram-of-thought.

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On the Diagram of Thought (DoT)

  • Diagram of Thought (DoT) enhances the reasoning capabilities of large language models (LLMs) by modeling iterative reasoning as a directed acyclic graph (DAG) grounded in Topos Theory.

  • By integrating propositions, critiques, refinements, and verifications into a unified DAG structure, DoT enables complex logical deductions beyond traditional linear or tree-based approaches.

  • Utilizing role-specific tokens, DoT generates detailed reasoning processes without external intervention.

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