AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filters
This work addresses the social implications of data curation for AI researchers and practitioners, highlighting biases in pretraining data filters.
The study tackled the under-scrutinized effects of data curation in large language model pretraining by analyzing how ten quality and English language identification filters impact webpages based on social and geographic contexts, revealing implicit preferences such as topical domain filtering and regional biases.
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.