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Register a new userDocumenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus
توثيق WebText Corpora الكبيرة: دراسة حالة على Colussal Clash Cruwled Corpus
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أدت نماذج اللغة الكبيرة إلى إحراز تقدم ملحوظ في العديد من مهام NLP، ويتحول الباحثون إلى نصائح نصية أكبر من أي وقت مضى لتدريبهم. يتم تقديم بعض أكبر الشركات المتاحة من خلال تجريف أجزاء مهمة من الإنترنت، ويتم إدخالها بشكل متكرر مع الحد الأدنى فقط من الوثائق. في هذا العمل، نقدم بعض الوثائق الأولى لجور الزحف النظيف الهائل (C4؛ Raffel et al.، 2020)، مجموعة بيانات تم إنشاؤها عن طريق تطبيق مجموعة من المرشحات إلى لقطة واحدة من الزحف المشترك. نبدأ بالتحقيق في المكان الذي جاءت فيه البيانات، وإيجاد كمية كبيرة من النص من مصادر غير متوقعة مثل براءات الاختراع ومواقع الويب العسكرية الأمريكية. ثم نستكشف محتوى النص نفسه، وإيجاد نص تم إنشاؤه بواسطة الجهاز (E.G.، من أنظمة الترجمة الآلية) وأمثلة التقييم من مجموعات بيانات NLP القياسية الأخرى. لفهم تأثير المرشحات المطبقة على إنشاء هذه البيانات، نقوم بتقييم النص الذي تمت إزالته، وإظهار أن تصفية Blocklist تزيل النص غير متناسب من الأفراد وأصحاب الأقليات. أخيرا، نستنتج بعض التوصيات حول كيفية إنشاء وتوثيق مجموعات بيانات الويب من كشط الإنترنت.
Large language models have led to remarkable progress on many NLP tasks, and researchers are turning to ever-larger text corpora to train them. Some of the largest corpora available are made by scraping significant portions of the internet, and are frequently introduced with only minimal documentation. In this work we provide some of the first documentation for the Colossal Clean Crawled Corpus (C4; Raffel et al., 2020), a dataset created by applying a set of filters to a single snapshot of Common Crawl. We begin by investigating where the data came from, and find a significant amount of text from unexpected sources like patents and US military websites. Then we explore the content of the text itself, and find machine-generated text (e.g., from machine translation systems) and evaluation examples from other benchmark NLP datasets. To understand the impact of the filters applied to create this dataset, we evaluate the text that was removed, and show that blocklist filtering disproportionately removes text from and about minority individuals. Finally, we conclude with some recommendations for how to created and document web-scale datasets from a scrape of the internet.
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