task_categories:question-answering language:en license:mit size_categories:n<1K format:json modality:text library:datasets library:pandas library:mlcroissant library:polars region:us camel question-answering chain-of-thought
---
authors:
- 0fg
description: Question-Answer dataset generated using CAMEL CoTDataGenerator and GPT-4o Mini
language:
- en
license: mit
pretty_name: 0fg/truthful_qa_CoT
size_categories: <1MB
tags:
- camel
- question-answering
- chain-of-thought
task_categories:
- question-answering
version: 0.1.0

---
# Dataset Card for TruthfulQA-CoT

## Dataset Details

### Dataset Description

This dataset is an augmented version of [TruthfulQA](https://huggingface.co/datasets/truthfulqa/truthful_qa), where Chain-of-Thought (CoT) reasoning has been applied to the original questions. The dataset was generated using [Camel-AI](https://github.com/camel-ai/camel) and GPT-4o Mini to enhance logical reasoning capabilities in smaller language models.

The dataset is valuable for fine-tuning smaller LLMs to improve CoT reasoning, helping models produce more structured and explainable responses.

- **Curated by:** 0fg
- **Language(s) (NLP):** English (en)
- **License:** MIT

### Dataset Sources

- **Original Dataset:** [TruthfulQA](https://huggingface.co/datasets/truthfulqa)
- **CAMEL-AI Repository:** [GitHub](https://github.com/camel-ai/camel)

### Dataset Structure

Each entry in the dataset follows a structured Chain-of-Thought prompt format:

```
prompt = f"""
Please think step by step and solve this problem: {question}
Existing content: {context}
Requirements:
1. Analyze the problem requirements
2. List the steps to solve the problem
3. Execute the solution process
4. Provide the final answer
Please explain the thought process of each step in detail.
"""
```

The dataset consists of:
- **Questions:** Extracted from TruthfulQA
- **CoT Responses:** Generated using CAMEL and GPT-4o Mini following the structured prompt

### Source Data

#### Data Collection and Processing

- Questions were taken from the TruthfulQA dataset.
- Manually removed a small number of low-quality questions that resulted in "no comment" answers.
- Responses were generated using Camel-AI's `CoTDataGenerator` and GPT-4o Mini.
- The data was formatted to encourage structured reasoning in LLMs.


### Contributions

Thanks to [TruthfulQA](https://huggingface.co/truthfulqa) for the original dataset.