TF1-EN-3M
Full name: klusai/ds-tf1-en-3m

# TF1-EN-3M: Three Million Synthetic Moral Fables for Open Language Models

**TF1-EN-3M** is a large-scale synthetic dataset of **3,000,000 English-language moral fables**, generated by instruction-tuned language models with no more than 8 billion parameters. The stories are aimed at child-friendly educational and moral reasoning applications and follow a consistent six-part narrative scaffold: `character → trait → setting → conflict → resolution → moral`.

## Dataset Characteristics

- **Size**: 3 million stories (~1B tokens)
- **Format**: JSON Lines, with detailed metadata including prompt elements, model configuration, generation time, token counts, and costs
- **Generation Models**: Evaluated across 10 open-weight LLMs; final dataset generated using `LLaMA-3.1-8B-Instruct` for optimal quality and cost balance
- **Story Structure**: Each story ends with an explicit moral and follows a template-driven structure
- **Target Audience**: Designed primarily for children aged 4–7 (age group B), with simple vocabulary and accessible narratives

## Motivation

Natural language processing lacks large, structured corpora of fables that combine creative storytelling with explicit moral lessons. Existing human-authored datasets like Aesop's Fables are limited in scale and diversity. TF1-EN-3M bridges this gap by:

- Demonstrating that mid-sized open models can reliably generate coherent, instructive stories
- Enabling research into value alignment, narrative intelligence, and low-resource model fine-tuning
- Offering a reproducible, cost-efficient alternative to proprietary LLM pipelines

## Summary of Content

Each entry in the dataset includes:

- A structured prompt with narrative elements
- The generated fable text
- Metadata (model name, inference time, token usage, cost, etc.)
- Quality assessments (via LLM-based scoring for grammar, creativity, moral clarity, and structure adherence)

## Use Cases

TF1-EN-3M is suitable for a wide range of tasks and applications:

- **Training** small or medium-sized LLMs for story generation or moral reasoning
- **Benchmarking** models on tasks like moral inference, story-to-moral mapping, or story quality evaluation
- **Educational AI tools**, such as interactive storytelling tutors or automated moral education platforms
- **Creative NLP** research, including literary analysis and narrative generation
- **Multilingual extension** by swapping out prompt elements for other languages

## Citation

If you use TF1-EN-3M, please cite:

```bibtex
@misc{nadas2025tf1en3m,
  title={TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open Language Models},
  author={Mihai Nădaș and Laura Dioșan and Andreea Tomescu and Andrei Pișcoran},
  year={2025},
  eprint={2504.20605},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}
```

## Dataset Access

- **Hugging Face**: [klusai/ds-tf1-en-3m](https://huggingface.co/datasets/klusai/ds-tf1-en-3m)
- **Generation & Evaluation Code**: [TinyFabulist GitHub](https://github.com/klusai/tinyfabulist)

Introducing paper: TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open Language Models