Difference between revisions of "Chain of Thought (CoT)"
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* [[Singularity]] ... [[Artificial Consciousness / Sentience|Sentience]] ... [[Artificial General Intelligence (AGI)| AGI]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ... [[Algorithm Administration#Automated Learning|Automated Learning]] | * [[Singularity]] ... [[Artificial Consciousness / Sentience|Sentience]] ... [[Artificial General Intelligence (AGI)| AGI]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ... [[Algorithm Administration#Automated Learning|Automated Learning]] | ||
| − | 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. | + | 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. |
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Revision as of 12:44, 25 May 2023
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- Singularity ... Sentience ... AGI ... Curious Reasoning ... Emergence ... Moonshots ... Explainable AI ... Automated Learning
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.
