Chain-of-Thought (CoT) Dataset

A Chain-of-Thought (CoT) Dataset is an LLM training dataset that is designed to enhance LLM reasoning capability by providing explicit step-by-step problem solving examples (that support LLM supervised fine-tuning tasks).

• AKA: CoT Dataset, Reasoning Chain Dataset.
• Context:
  • It can typically contain Explicit Reasoning Processes with chain-of-thought reasoning steps.
  • It can typically demonstrate Complex Problem Solving using chain-of-thought intermediate reasoning states.
  • It can typically enhance Large Language Model Reasoning Capability through chain-of-thought supervised fine-tuning.
  • It can typically include Thinking Section Markup for chain-of-thought process delineation.
  • It can typically separate Problem Statement from chain-of-thought reasoning process.
  • ...
  • It can often capture Human Expert Reasoning in chain-of-thought annotation format.
  • It can often support Multi-Step Solution Generation with chain-of-thought explanation.
  • It can often improve Model Transparency through chain-of-thought reasoning visibility.
  • It can often reduce Hallucination Rate through chain-of-thought verification steps.
  • ...
  • It can range from being a Simple Chain-of-Thought Dataset to being a Complex Chain-of-Thought Dataset, depending on its problem complexity.
  • It can range from being a Domain-Specific Chain-of-Thought Dataset to being a General-Purpose Chain-of-Thought Dataset, depending on its application scope.
  • It can range from being a Small-Scale Chain-of-Thought Dataset to being a Large-Scale Chain-of-Thought Dataset, depending on its example count.
  • ...
  • It can have Task Input such as problem statements, querys.
  • It can have Task Output such as step-by-step reasonings, final answers.
  • It can have Task Performance Measures such as reasoning accuracy, step validity, and solution correctness.
  • ...
• Examples:
  • Chain-of-Thought Dataset Categories, such as:
    • Mathematical Chain-of-Thought Datasets, such as:
      • Arithmetic Chain-of-Thought Dataset for arithmetic problem solving.
      • Algebraic Chain-of-Thought Dataset for algebra problem solving.
      • Geometric Chain-of-Thought Dataset for geometry problem solving.
    • Code Chain-of-Thought Datasets, such as:
      • Programming Chain-of-Thought Dataset for code generation.
      • Debugging Chain-of-Thought Dataset for error identification.
      • Algorithm Chain-of-Thought Dataset for computational problem solving.
    • Logical Chain-of-Thought Datasets, such as:
      • Deductive Chain-of-Thought Dataset for logical deduction.
      • Inductive Chain-of-Thought Dataset for pattern recognition.
      • Abductive Chain-of-Thought Dataset for hypothesis generation.
  • Chain-of-Thought Dataset Implementations, such as:
    • NVIDIA Llama-Nemotron Chain-of-Thought Dataset (2024), with 15.2 million reasoning examples.
    • GSM8K Chain-of-Thought Dataset, for grade school math problem solving.
    • CommonsenseQA Chain-of-Thought Dataset, for common sense reasoning.
  • ...
• Counter-Examples:
  • Standard Fine-Tuning Datasets, which lack explicit reasoning steps.
  • Input-Output Pair Datasets, which focus on final answers without intermediate reasoning.
  • Instruction-Following Datasets, which emphasize task completion rather than reasoning process.
• See: Reasoning Dataset, Large Language Model Training, Supervised Fine-Tuning, Step-by-Step Reasoning, Model Reasoning Capability.

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References

2025-03-19

• Perplexity.ai
  • Question: What are Chain-of-Thought Datasets? Please explain their structure, types, and importance for enhancing reasoning capabilities in large language models. Include information about how these datasets are used in Supervised Fine-Tuning (SFT), notable examples like NVIDIA's dataset, and how they differ from standard training datasets.
  • Answer: Chain-of-Thought (CoT) datasets are specialized training resources designed to develop reasoning capabilities in large language models. These datasets contain step-by-step reasoning examples that demonstrate how to break down and solve complex problems, enabling models to learn explicit reasoning patterns rather than just memorizing input-output pairs.
    • Understanding Chain-of-Thought Datasets:
      • Basic Definition:
        • Chain-of-Thought datasets are collections of examples that explicitly capture reasoning processes, typically showing a progression from problem statement to solution through clear intermediate steps.
        • These datasets are used in Supervised Fine-Tuning (SFT) to enhance an LLM's ability to reason through complex problems rather than simply generating plausible-sounding answers.
        • The distinctive feature of CoT datasets is their explicit reasoning structure, often formatted with special tags or sections that separate the thinking process from the final answer.
    • Types of Chain-of-Thought Datasets:
      • Mathematical Reasoning Datasets:
        • These datasets focus on mathematical problem-solving across various domains including arithmetic, algebra, geometry, calculus, and statistics.
        • Example structure: `Problem: If a triangle has sides of length 3, 4, and 5, what is its area? Thinking: I know this is a right triangle (3-4-5 is a Pythagorean triple). The area of a right triangle is (base × height)/2. Using sides 3 and 4 as the base and height: Area = (3 × 4)/2 = 6. Answer: 6 square units`
        • Advantages:
          • Mathematical reasoning has clear, verifiable steps.
          • Problems can be scaled in difficulty.
          • Results are objectively correct or incorrect.
        • Limitations:
          • May not generalize to less structured reasoning domains.
          • Risk of teaching formula application without deeper conceptual understanding.
      • Code Reasoning Datasets:
        • These datasets demonstrate the process of writing, debugging, and optimizing code solutions to programming problems.
        • Example from NVIDIA's dataset: `User: Given a list of integers, write a function that returns a new list where each integer is multiplied by its index in the original list.`
    • Supervised Fine-Tuning for Reasoning:
      • Process Overview:
        • The post-training process used to enhance reasoning capabilities in large language models is called Supervised Fine-Tuning (SFT) with reasoning datasets.
        • This specific approach focuses on training models to develop step-by-step reasoning skills rather than simply generating plausible-sounding outputs.
      • Dataset Structure:
        • The reasoning datasets used for this post-training process have a distinctive structure:
          • Input queries that present problems requiring multi-step solutions.
          • Detailed thinking sections (often marked with `<think>` tags) that demonstrate the reasoning process.
          • Final answers that provide concise solutions based on the reasoning.
    • NVIDIA's Approach:
      • Dataset Characteristics:
        • NVIDIA's "Llama-Nemotron-Post-Training-Dataset-v1" is a comprehensive reasoning dataset containing examples across various domains.
        • This dataset contains approximately 15.2 million rows of training examples, with a specific subset focused on code-related reasoning (about 1.56 million examples).
        • Each entry in NVIDIA's dataset contains:
          • Input prompts requiring reasoning.
          • Detailed thinking processes.
          • Categorical metadata including license information and generator information.
          • Indicators of whether examples were used in training.
    • Importance and Impact:
      • Skill Development:
        • This reasoning-focused post-training approach has become essential for developing LLMs with strong problem-solving abilities.
        • By exposing models to explicit reasoning patterns, they learn to:
          • Break down complex problems into manageable steps.
          • Consider multiple approaches to a solution.
          • Verify their reasoning before providing final answers.
          • Explain their thought process in a human-understandable way.
  • Citations:

[1] https://ppl-ai-file-upload.s3.amazonaws.com/web/direct-files/16497056/27790802-8b9e-4662-b43b-2d3ab6efa5a3/paste.txt

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