What's in the Box? A Preliminary Analysis of Undesirable Content in the Common Crawl Corpus
This highlights a critical data quality issue for AI researchers and developers, as such content can negatively impact language model training and outputs.
The study analyzed the Common Crawl corpus, a large web dataset used for training language models, and found it contains significant undesirable content like hate speech and sexually explicit material, even after filtering.
Whereas much of the success of the current generation of neural language models has been driven by increasingly large training corpora, relatively little research has been dedicated to analyzing these massive sources of textual data. In this exploratory analysis, we delve deeper into the Common Crawl, a colossal web corpus that is extensively used for training language models. We find that it contains a significant amount of undesirable content, including hate speech and sexually explicit content, even after filtering procedures. We discuss the potential impacts of this content on language models and conclude with future research directions and a more mindful approach to corpus collection and analysis.