• Beyond a Single Extractor: Re-thinking HTML-to-Text Extraction for LLM Pretraining Beyond a Single Extractor: Re-thinking HTML-to-Text Extraction for LLM Pretraining AuthorsJeffrey Liâ  , Josh Gardner, Doug Kang, Fangping Shi, Karanjeet Singh, Chun-Liang Li, Herumb Shandilyaâ ¡, David Hallâ ¡, Oncel Tuzel, Percy Liangâ ¡, Ludwig Schmidtâ ¡, Hadi Pour Ansari, Fartash Faghri View publication Copy Bibtex One of the first pre-processing steps for constructing web-scale LLM pretraining datasets involves extracting text from HTML. • Despite the immense diversity of web content, existing open-source datasets predominantly apply a single fixed extractor to all webpages. • In this work, we investigate whether this practice leads to suboptimal coverage and utilization of Internet data. • We first show that while different extractors may lead to similar model performance on standard language understanding tasks, the pages surviving a fixed filtering pipeline can differ substantially. • This suggests a simple intervention: by taking a Union over different extractors, we can increase the token yield of DCLM-Baseline by up to 71% while maintaining benchmark performance. • We further show that for structured content such as tables and code blocks, extractor choice can significantly impact downstream task performance, with differences of up to 10 percentage points (p.p.) on WikiTQ and 3 p.p.

Article Summaries:

  • One of the first pre-processing steps for constructing web-scale LLM pretraining datasets involves extracting text from HTML. Despite the immense diversity of web content, existing open-source datasets predominantly apply a single fixed extractor to all webpages. In this work, we investigate whether this practice leads to suboptimal coverage and utilization of Internet data. We first show that while different extractors may lead to similar model performance on standard language understanding tasks, the pages surviving a fixed filtering pipeline can differ substantially. This suggests a simple

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