Dan Braun — accepted AI-conference papers

2025

Parameterized Synthetic Text Generation with SimpleStories

Lennart Finke, Chandan Sreedhara, Thomas Dooms, Mat Allen, Juan Diego Rodriguez, Noa Nabeshima +2

NeurIPS 2025poster

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 divers…

Cited by 0 Source ↗code ↗Scholar ↗

2024

Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning

Dan Braun, Jordan Taylor, Nicholas Goldowsky-Dill, Lee Sharkey

NeurIPS 2024poster

Identifying the features learned by neural networks is a core challenge in mechanistic interpretability. Sparse autoencoders (SAEs), which learn a sparse, overcomplete dictionary that reconstructs a network's internal activations, have been used to identify these features. However, SAEs may learn mo…

Cited by 20 Source ↗PDF ↗code ↗Scholar ↗