LLM Chain-of-Thought Training Task

An LLM Chain-of-Thought Training Task is an LLM supervised fine-tuning task that develops reasoning capabilities in large language models through LLM explicit step-by-step problem solving.

• AKA: LLM CoT Training, LLM Reasoning Training Task, LLM Step-by-Step Reasoning Training.
• Context:
  • It can typically utilize LLM chain-of-thought datasets containing LLM explicit reasoning processes and LLM intermediate reasoning steps.
  • It can typically train LLMs to generate LLM step-by-step solutions rather than just LLM final answers.
  • It can typically enhance LLM problem decomposition ability through exposure to LLM multi-step reasoning examples.
  • It can typically improve LLM mathematical reasoning, LLM logical deduction, and LLM causal reasoning.
  • It can typically incorporate LLM thinking annotations such as "<think>" tags to distinguish LLM reasoning process from LLM final response.
  • ...
  • It can often reduce LLM hallucinations by encouraging LLM structured thinking before producing LLM answers.
  • It can often improve LLM explainability by making LLM reasoning processes visible to LLM users.
  • It can often increase LLM performance on LLM complex tasks requiring LLM multi-step inference.
  • It can often utilize LLM human expert annotations to create LLM gold standard reasoning examples.
  • It can often employ LLM self-supervision techniques where LLMs generate their own LLM reasoning paths.
  • ...
  • It can range from being a Simple LLM Chain-of-Thought Training Task to being a Complex LLM Chain-of-Thought Training Task, depending on its LLM reasoning depth.
  • It can range from being a Domain-Specific LLM Chain-of-Thought Training Task to being a General-Purpose LLM Chain-of-Thought Training Task, depending on its LLM application scope.
  • It can range from being a Manual LLM Chain-of-Thought Training Task to being an Automated LLM Chain-of-Thought Training Task, depending on its LLM data generation approach.
  • ...
  • It can have LLM Task Input: LLM chain-of-thought datasets, LLM reasoning examples, LLM problem-solution pairs
    • LLM Optional Input: LLM base model weights, LLM reasoning prompt templates, LLM verification datasets
  • It can have LLM Task Output: LLM reasoning-enhanced model, LLM reasoning benchmark scores, LLM reasoning capability evaluation
  • It can have LLM Task Performance Measures such as LLM reasoning accuracy, LLM step validity, LLM solution correctness, and LLM reasoning transparency
  • ...
• Examples:
  • LLM Chain-of-Thought Training Task Categorys, such as:
    • LLM Mathematical Chain-of-Thought Training Tasks, such as:
      • LLM Arithmetic Reasoning Training Task for teaching LLM step-by-step calculation.
      • LLM Algebraic Problem Solving Training Task for improving LLM equation manipulation.
      • LLM Geometric Reasoning Training Task for enhancing LLM spatial reasoning.
    • LLM Logical Chain-of-Thought Training Tasks, such as:
      • LLM Deductive Reasoning Training Task for strengthening LLM logical inference.
      • LLM Analytical Thinking Training Task for improving LLM pattern recognition.
      • LLM Syllogistic Reasoning Training Task for enhancing LLM premise-conclusion relationship understanding.
    • LLM Verbal Chain-of-Thought Training Tasks, such as:
      • LLM Reading Comprehension Training Task for developing LLM text analysis skills.
      • LLM Argument Evaluation Training Task for improving LLM critical thinking.
      • LLM Language Problem Solving Training Task for enhancing LLM linguistic reasoning.
  • LLM Chain-of-Thought Training Techniques, such as:
    • LLM Explicit Annotation Techniques, such as:
      • LLM Think-Aloud Protocol Training using LLM verbalized reasoning processes.
      • LLM Structured Reasoning Template Training with LLM formal reasoning frameworks.
    • LLM Self-Improvement Techniques, such as:
      • LLM Iterative Self-Refinement Training where LLMs critique and improve their own LLM reasoning.
      • LLM Bootstrapped Reasoning Training generating LLM synthetic reasoning datasets.
  • LLM Chain-of-Thought Training Implementations, such as:
    • Google PaLM CoT Training Implementation (2022), using LLM few-shot exemplars.
    • Anthropic Constitutional AI Training (2023), incorporating LLM self-critique reasoning.
    • NVIDIA LLama-Nemotron CoT Training (2024), featuring LLM 15.2 million reasoning examples.
  • ...
• Counter-Examples:
  • LLM Standard Fine-Tuning Task, which focuses on general LLM instruction following without explicit LLM reasoning steps.
  • LLM RLHF Training Task, which optimizes LLM outputs based on LLM human preference rather than teaching LLM reasoning processes.
  • LLM Chain-of-Thought Prompting Technique, which elicits LLM reasoning at LLM inference time without specific LLM training.
  • LLM Factual Knowledge Training Task, which emphasizes LLM fact memorization rather than LLM reasoning capability.
  • LLM Retrieval-Augmented Task, which focuses on LLM external information retrieval rather than LLM internal reasoning.
• See: LLM Reasoning Dataset, LLM Chain-of-Thought Dataset, LLM Supervised Fine-Tuning, LLM Reasoning Benchmark, LLM Step-by-Step Problem Solving, LLM Explainable AI.

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