TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open Language Models
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915972870307840 |
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| author | Nadas, Mihai Diosan, Laura Piscoran, Andrei Tomescu, Andreea |
| author_facet | Nadas, Mihai Diosan, Laura Piscoran, Andrei Tomescu, Andreea |
| contents | Moral stories are a time-tested vehicle for transmitting values, yet modern NLP lacks a large, structured corpus that couples coherent narratives with explicit ethical lessons. We present TF1-EN-3M, to our knowledge the first open dataset of three million English-language fables generated exclusively by instruction-tuned models no larger than 8B parameters. Each story follows a six-slot scaffold (character -> trait -> setting -> conflict -> resolution -> moral), produced through a combinatorial prompt engine that guarantees genre fidelity while covering a broad thematic space.
A fully reproducible evaluation pipeline employs a panel of open-weight LLM judges from distinct model families, scoring grammar, creativity, moral clarity, and template adherence, complemented by reference-free diversity and readability metrics. Among ten open-weight generator candidates, an 8B-parameter Llama-3 variant delivers the best quality-cost trade-off, producing high-scoring fables on consumer hardware at approximately $0.135 per 1,000 fables.
We release the dataset, generation code, evaluation scripts, and full metadata under a permissive license, enabling exact reproducibility and cost benchmarking. TF1-EN-3M opens avenues for research in instruction following, narrative intelligence, value alignment, and child-friendly educational AI -- demonstrating that large-scale moral storytelling requires neither proprietary giant models nor proprietary evaluation infrastructure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_20605 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open Language Models Nadas, Mihai Diosan, Laura Piscoran, Andrei Tomescu, Andreea Computation and Language Artificial Intelligence Digital Libraries Machine Learning Moral stories are a time-tested vehicle for transmitting values, yet modern NLP lacks a large, structured corpus that couples coherent narratives with explicit ethical lessons. We present TF1-EN-3M, to our knowledge the first open dataset of three million English-language fables generated exclusively by instruction-tuned models no larger than 8B parameters. Each story follows a six-slot scaffold (character -> trait -> setting -> conflict -> resolution -> moral), produced through a combinatorial prompt engine that guarantees genre fidelity while covering a broad thematic space. A fully reproducible evaluation pipeline employs a panel of open-weight LLM judges from distinct model families, scoring grammar, creativity, moral clarity, and template adherence, complemented by reference-free diversity and readability metrics. Among ten open-weight generator candidates, an 8B-parameter Llama-3 variant delivers the best quality-cost trade-off, producing high-scoring fables on consumer hardware at approximately $0.135 per 1,000 fables. We release the dataset, generation code, evaluation scripts, and full metadata under a permissive license, enabling exact reproducibility and cost benchmarking. TF1-EN-3M opens avenues for research in instruction following, narrative intelligence, value alignment, and child-friendly educational AI -- demonstrating that large-scale moral storytelling requires neither proprietary giant models nor proprietary evaluation infrastructure. |
| title | TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open Language Models |
| topic | Computation and Language Artificial Intelligence Digital Libraries Machine Learning |
| url | https://arxiv.org/abs/2504.20605 |