English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filters