Chain-of-Thought Reasoning Technique

A Chain-of-Thought Reasoning Technique is a reasoning technique that enables step-by-step problem solving through intermediate reasoning steps (to improve complex task performance).

• AKA: CoT Reasoning Technique, Chain-of-Thought Prompting, Step-by-Step Reasoning Method.
• Context:
  • It can typically decompose Chain-of-Thought Complex Problems into chain-of-thought manageable steps.
  • It can typically generate Chain-of-Thought Reasoning Paths through chain-of-thought sequential decomposition.
  • It can typically improve Chain-of-Thought Mathematical Solutions via chain-of-thought arithmetic steps.
  • It can typically enhance Chain-of-Thought Logical Performance through chain-of-thought inference chains.
  • It can typically facilitate Chain-of-Thought Problem Understanding by chain-of-thought explicit verbalization.
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  • It can often enable Chain-of-Thought Zero-Shot Reasoning through chain-of-thought prompt instructions.
  • It can often support Chain-of-Thought Few-Shot Learning via chain-of-thought example demonstrations.
  • It can often reduce Chain-of-Thought Reasoning Errors through chain-of-thought verification steps.
  • It can often increase Chain-of-Thought Model Interpretability via chain-of-thought transparent processes.
  • ...
  • It can range from being a Simple Chain-of-Thought Reasoning Technique to being a Complex Chain-of-Thought Reasoning Technique, depending on its chain-of-thought reasoning depth.
  • It can range from being a Manual Chain-of-Thought Reasoning Technique to being an Automated Chain-of-Thought Reasoning Technique, depending on its chain-of-thought generation method.
  • It can range from being a Domain-Specific Chain-of-Thought Reasoning Technique to being a General-Purpose Chain-of-Thought Reasoning Technique, depending on its chain-of-thought application scope.
  • It can range from being a Linear Chain-of-Thought Reasoning Technique to being a Tree-of-Thought Reasoning Technique, depending on its chain-of-thought reasoning structure.
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  • It can integrate with AI Reasoning Model for chain-of-thought model implementation.
  • It can connect to Chain-of-Thought (CoT) Dataset for chain-of-thought training data.
  • It can interface with Chain of Draft (CoD) Prompting Method for chain-of-thought efficiency optimization.
  • It can communicate with Mathematical Reasoning Benchmark for chain-of-thought performance evaluation.
  • It can synchronize with Long-Horizon Reasoning System for chain-of-thought extended reasoning.
  • ...
• Example(s):
  • Zero-Shot CoT using "Let's think step by step" for chain-of-thought reasoning initiation.
  • Few-Shot CoT with chain-of-thought worked examples for chain-of-thought mathematical problems.
  • Self-Consistency CoT using chain-of-thought multiple paths for chain-of-thought answer verification.
  • Tree-of-Thoughts exploring chain-of-thought branching paths for chain-of-thought complex reasoning.
  • ...
• Counter-Example(s):
  • Direct Prompting, which lacks chain-of-thought intermediate steps.
  • In-Context Learning, which uses pattern matching rather than chain-of-thought explicit reasoning.
  • Retrieval-Augmented Generation, which relies on external knowledge rather than chain-of-thought reasoning chains.
• See: Reasoning Technique, AI Reasoning Model, Chain-of-Thought (CoT) Dataset, Chain of Draft (CoD) Prompting Method, Complex Reasoning Task, Thinking Small, 2022 ChainofThoughtPromptingElicitsR.

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