  NLP researchers need more, higher-quality text datasets. Human-labeled
datasets are expensive to collect, while datasets collected via automatic
retrieval from the web such as WikiBio are noisy and can include undesired
biases. Moreover, data sourced from the web is often included in datasets used
to pretrain models, leading to inadvertent cross-contamination of training and
test sets. In this work we introduce a novel method for efficient dataset
curation: we use a large language model to provide seed generations to human
raters, thereby changing dataset authoring from a writing task to an editing
task. We use our method to curate SynthBio - a new evaluation set for WikiBio -
composed of structured attribute lists describing fictional individuals, mapped
to natural language biographies. We show that our dataset of fictional
biographies is less noisy than WikiBio, and also more balanced with respect to
gender and nationality.
