Difference between revisions of "Chain of Thought (CoT)"
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* [https://arxiv.org/abs/2305.08291 2305.08291 Large Language Model Guided Tree-of-Thought | Jieyi Long -arXiv.org] | * [https://arxiv.org/abs/2305.08291 2305.08291 Large Language Model Guided Tree-of-Thought | Jieyi Long -arXiv.org] | ||
* [https://arxiv.org/abs/2305.10601 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] | * [https://arxiv.org/abs/2305.10601 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] | ||
| − | * [https://the-decoder.com/system-2-inspired-method-enhances-gpt-4s-logic-capability GPT-4's logic capabilities can be enhanced with a "Tree of Thoughts" | + | * [https://the-decoder.com/system-2-inspired-method-enhances-gpt-4s-logic-capability GPT-4's logic capabilities can be enhanced with a "Tree of Thoughts" | Maximilian Schreiner - The Decoder] |
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"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 [[Large Language Model (LLM)|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 [[Large Language Model (LLM)|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. | "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 [[Large Language Model (LLM)|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 [[Large Language Model (LLM)|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. | ||
<youtube>G9dRK9TNAeg</youtube> | <youtube>G9dRK9TNAeg</youtube> | ||
Revision as of 12:15, 25 May 2023
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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.
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.
