On the Diagram of Thought
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.
66e928df2ff641e80cf92362
Yifan Zhang
claimed_verified
2024-09-17T07:11:13.884Z
647bf082aba7062fe5c51ca9
https://cdn-avatars.huggingface.co/v1/production/uploads/647bf082aba7062fe5c51ca9/p4lY9IjHiWZETKmFq1mtU.jpeg
Yifan Zhang
user
yifAI
66e928df2ff641e80cf92363
Yang Yuan
66e928df2ff641e80cf92364
Andrew Chi-Chih Yao
On the Diagram of Thought
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.
The Diagram of Thought (DoT) framework models iterative reasoning in LLMs as a DAG, leveraging auto-regressive prediction and Topos Theory to enhance reasoning consistency and efficiency.
Diagram of Thought (DoT) large language models (LLMs) directed acyclic graph (DAG) iterative reasoning propositions critiques refinements verifications auto-regressive next-token prediction role-specific tokens Topos Theory logical consistency soundness