TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open
  Language Models
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.
68131e73f0f2a4d8b2d4b06a
Mihai Nadas
claimed_verified
2025-05-02T06:34:48.889Z
642bcb8ae5b6823cde9301bd
/avatars/cddd29afbdbc2fbea90612567090147b.svg
Mihai Dan Nadăș
user
mihainadas
68131e73f0f2a4d8b2d4b06b
Laura Diosan
68131e73f0f2a4d8b2d4b06c
Andrei Piscoran
extracted_confirmed
2025-05-01T07:11:31.916Z
67b2344d0ce2aaa57c8c9997
/avatars/c3fecce799152db985418f64c4b62f65.svg
Andrei Piscoran
user
andreiPiscoran
68131e73f0f2a4d8b2d4b06d
Andreea Tomescu
extracted_pending
2025-05-01T07:10:43.780Z
677e4393ef848c5a5352d082
/avatars/bcb60ead58969601e2911053550fec62.svg
Andreea Tomescu
user
andreeatomescu
TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open
  Language Models
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.
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.
instruction-tuned models combinatorial prompt engine GPT-based critic diversity metrics readability metrics Llama-3 narrative intelligence value alignment child-friendly educational AI