  Large language models' (LLMs) abilities are drawn from their pretraining
data, and model development begins with data curation. However, decisions
around what data is retained or removed during this initial stage are
under-scrutinized. In our work, we ground web text, which is a popular
pretraining data source, to its social and geographic contexts. We create a new
dataset of 10.3 million self-descriptions of website creators, and extract
information about who they are and where they are from: their topical
interests, social roles, and geographic affiliations. Then, we conduct the
first study investigating how ten "quality" and English language identification
(langID) filters affect webpages that vary along these social dimensions. Our
experiments illuminate a range of implicit preferences in data curation: we
show that some quality classifiers act like topical domain filters, and langID
can overlook English content from some regions of the world. Overall, we hope
that our work will encourage a new line of research on pretraining data
curation practices and its social implications.
