Chain of Thought (CoT)

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AI can generate text that follows a logical and coherent sequence of ideas, building on previous statements to form a chain of thought. Chain of thought (CoT) is a method that breaks a problem down into a series of intermediate reasoning steps. It has significantly improved the ability of Large Language Model (LLM)s to perform complex reasoning. It is the current state-of-the-art in teaching LLMs how to take action. An example of CoT prompting can be seen in solving a simple word problem. Without CoT prompting, GPT-3 (davinci-003) fails to solve the problem. However, with CoT prompting, GPT-3 (davinci-003) successfully solves the same problem by breaking it down into intermediate reasoning steps.

Tree of Thoughts (ToT)

• PT-4's logic capabilities can be enhanced with a "Tree of Thoughts"
• 2305.08291 Large Language Model Guided Tree-of-Thought | Jieyi Long -arXiv.org
• 2305.10601 Tree of Thoughts: Deliberate Problem Solving with Large | S. Yao, D. Yu, J. Zhao, I. Shafran, T. Griffiths, Y. Cao, K. Narasimhanar - Xiv.org
• GPT-4's logic capabilities can be enhanced with a "Tree of Thoughts" | Maximilian Schreiner - The Decoder

"Tree of Thoughts" is a new framework for inferencing language models like GPT-4, inspired by prompt engineering methods like Chain of Thought. It is a novel approach aimed at improving the problem-solving capabilities of auto-regressive Large Language Model (LLM)s by allowing them to explore multiple reasoning paths over thoughts. To implement ToT as a software system, an LLM is augmented with additional modules including a prompter agent, a checker module, a memory module, and a ToT controller. These modules engage in a multi-round conversation with the LLM to solve a given problem. The memory module records the conversation and state history of the problem-solving process, which allows the system to backtrack to previous steps of the thought-process and explore other directions from there.