title: TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open
    Language Models
summary: 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 close this gap with TF1-EN-3M, 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 hybrid evaluation pipeline blends (i) a GPT-based critic that scores
  grammar, creativity, moral clarity, and template adherence with (ii)
  reference-free diversity and readability metrics. Among ten open-weight
  candidates, an 8B-parameter Llama-3 variant delivers the best quality-speed
  trade-off, producing high-scoring fables on a single consumer GPU (<24 GB VRAM)
  at approximately 13.5 cents 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 no longer requires
  proprietary giant models.
paper_authors._id: 68131e73f0f2a4d8b2d4b06a
paper_authors.name: Mihai Nadas
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-02T06:34:48.889Z
paper_authors._id: 642bcb8ae5b6823cde9301bd
paper_authors.avatarUrl: /avatars/cddd29afbdbc2fbea90612567090147b.svg
paper_authors.fullname: Mihai Dan Nadăș
paper_authors.type: user
paper_authors.user: mihainadas
paper_authors._id: 68131e73f0f2a4d8b2d4b06b
paper_authors.name: Laura Diosan
paper_authors._id: 68131e73f0f2a4d8b2d4b06c
paper_authors.name: Andrei Piscoran
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-01T07:11:31.916Z
paper_authors._id: 67b2344d0ce2aaa57c8c9997
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/67b2344d0ce2aaa57c8c9997/LSMjuQNjRsUllUQyt9vNo.jpeg
paper_authors.fullname: Andrei Piscoran
paper_authors.type: user
paper_authors.user: andreiPiscoran
paper_authors._id: 68131e73f0f2a4d8b2d4b06d
paper_authors.name: Andreea Tomescu
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-05-01T07:10:43.780Z
paper_authors._id: 677e4393ef848c5a5352d082
paper_authors.avatarUrl: /avatars/bcb60ead58969601e2911053550fec62.svg
paper_authors.fullname: Andreea Tomescu
paper_authors.type: user
paper_authors.user: andreeatomescu
paper_title: TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open
    Language Models
paper_summary: 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 close this gap with TF1-EN-3M, 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 hybrid evaluation pipeline blends (i) a GPT-based critic that scores
  grammar, creativity, moral clarity, and template adherence with (ii)
  reference-free diversity and readability metrics. Among ten open-weight
  candidates, an 8B-parameter Llama-3 variant delivers the best quality-speed
  trade-off, producing high-scoring fables on a single consumer GPU (<24 GB VRAM)
  at approximately 13.5 cents 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 no longer requires
  proprietary giant models.
paper_ai_summary: A new dataset, TF1-EN-3M, uses instruction-tuned models to generate three million English fables following a structured format, evaluated using a combination of automated metrics and human judgments, and released under a permissive license.
paper_ai_keywords: instruction-tuned models combinatorial prompt engine GPT-based critic diversity metrics readability metrics Llama-3 narrative intelligence value alignment child-friendly educational AI