TF1-EN-3M：三百万合成道德寓言用于训练小型开放语言模型
TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open Language Models
AI总结：
本文提出TF1-EN-3M数据集，包含三百万英文寓言，用于训练小型开放语言模型，展示通过指令微调模型生成高质量寓言的方法，验证了无需大模型即可实现大规模道德叙事的可能性。
摘要：
道德故事是传承价值观的传统载体，但现代NLP缺乏将连贯叙述与明确伦理教训结合的大型结构化语料库。我们提出了TF1-EN-3M，据我们所知，这是首个开放数据集，包含三百万个由不超过8B参数的指令微调模型生成的英文寓言。每个故事遵循六槽位框架（角色->特质->场景->冲突->解决->道德），通过组合提示引擎生成，确保题材忠实性的同时覆盖广泛主题空间。一个完全可复现的评估流程采用不同模型家族的开放权重LLM评委，评分语法、创造力、道德清晰度和模板遵循性，并辅以无参考多样性及可读性指标。在十种开放权重生成器候选者中，8B参数的Llama-3变体在质量-成本平衡上表现最佳，可在消费级硬件上以约每1000个寓言0.135美元的成本生成高分寓言。我们以宽松许可发布数据集、生成代码、评估脚本和完整元数据，使精确复现和成本基准测试成为可能。TF1-EN-3M为指令跟随、叙事智能、价值对齐和儿童友好型教育AI研究开辟了新途径，证明大规模道德叙事无需专有巨型模型或专有评估基础设施。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
