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arXiv · 2504.09184

Parameterized Synthetic Text Generation with SimpleStories

Lennart Finke·Chandan Sreedhara·Thomas Dooms·Mat Allen·Emerald Zhang·Juan Diego Rodriguez·Noa Nabeshima·Thomas Marshall·Dan Braun
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Abstract

We present SimpleStories, a large synthetic story dataset in simple language, consisting of 2 million samples each in English and Japanese. Through parameterizing prompts at multiple levels of abstraction, we achieve control over story characteristics at scale, inducing syntactic and semantic diversity. Ablations on a newly trained model suite show improved sample efficiency and model interpretability compared to the TinyStories dataset. We open-source all constituent parts of model creation, hoping to enable novel ways to study the end-to-end training process. As a byproduct, we move the frontier regarding the fewest-parameter language model that outputs grammatical natural language.

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Lennart Finke, Chandan Sreedhara, Thomas Dooms, Mat Allen, Emerald Zhang, Juan Diego Rodriguez, Noa Nabeshima, Thomas Marshall, Dan Braun. 2025-04-12. Parameterized Synthetic Text Generation with SimpleStories. https://arxiv.org/abs/2504.09184

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