“Automated tools can help identify gibberish or statements that lack citations, but helping human editors determine whether a source actually backs up a claim is a much more complex task — one that requires an AI system’s depth of understanding and analysis.

Building on Meta AI’s research and advancements, we’ve developed the first model capable of automatically scanning hundreds of thousands of citations at once to check whether they truly support the corresponding claims. It’s open-sourced here, and you can see a demo of our verifier here. As a knowledge source for our model, we created a new dataset of 134 million public webpages — an order of magnitude larger and significantly more intricate than ever used for this sort of research. It calls attention to questionable citations, allowing human editors to evaluate the cases most likely to be flawed without having to sift through thousands of properly cited statements. If a citation seems irrelevant, our model will suggest a more applicable source, even pointing to the specific passage that supports the claim. Eventually, our goal is to build a platform to help Wikipedia editors systematically spot citation issues and quickly fix the citation or correct the content of the corresponding article at scale….”