NeurIPS Conference 2025 Conference Paper
Parameterized Synthetic Text Generation with SimpleStories
- Lennart Finke
- Chandan Sreedhara
- Thomas Dooms
- Mat Allen
- Juan Rodriguez
- Noa Nabeshima
- Thomas Marshall
- Dan Braun
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 tiny model suite then show improved sample efficiency and model interpretability in comparison with 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 with regards to the fewest-parameter language model that outputs grammatical English.