Craw4LLM: Efficient Web Crawling for LLM Pretraining
Authors: name: Shi Yu, name: Zhiyuan Liu, name: Chenyan Xiong

Web crawl is a main source of large language models' (LLMs) pretraining data,
but the majority of crawled web pages are discarded in pretraining due to low
data quality. This paper presents Crawl4LLM, an efficient web crawling method
that explores the web graph based on the preference of LLM pretraining.
Specifically, it leverages the influence of a webpage in LLM pretraining as the
priority score of the web crawler's scheduler, replacing the standard graph
connectivity based priority. Our experiments on a web graph containing 900
million webpages from a commercial search engine's index demonstrate the
efficiency of Crawl4LLM in obtaining high-quality pretraining data. With just
21% URLs crawled, LLMs pretrained on Crawl4LLM data reach the same downstream
performances of previous crawls, significantly reducing the crawling waste and
alleviating the burdens on websites. Our code is publicly available at
https://github.com/cxcscmu/Crawl4LLM.