Craw4LLM: Efficient Web Crawling for LLM Pretraining (2502.13347v3)

Abstract: Web crawl is a main source of LLMs' (LLMs) pretraining data, but the majority of crawled web pages are discarded in pretraining due to low data quality. This paper presents Craw4LLM, 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 Craw4LLM in obtaining high-quality pretraining data. With just 21% URLs crawled, LLMs pretrained on Craw4LLM 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/Craw4LLM.

• The paper presents Craw4LLM, which scores webpages by their LLM pretraining influence, reducing crawled URLs to 21% of traditional methods.
• The methodology shifts focus from graph connectivity to content relevance using an iterative scoring function and priority queue for data selection.
• Experimental results show Craw4LLM achieves 95% of oracle performance on key tasks, improving LLM quality while cutting computational waste.

An Expert Overview of "Craw4LLM: Efficient Web Crawling for LLM Pretraining"

The paper "Craw4LLM: Efficient Web Crawling for LLM Pretraining" by Shi Yu, Zhiyuan Liu, and Chenyan Xiong introduces a novel methodology for optimizing web crawling in the context of LLM pretraining. Traditional web crawling techniques, such as those employed by Common Crawl, are inefficient for LLM data acquisition because they often prioritize graph connectivity metrics. As a result, an overwhelming majority of crawled data is discarded due to low relevance to LLM pretraining, leading not only to computational waste but also to ethical concerns regarding unnecessary web traffic.

Methodology

Craw4LLM challenges the conventional paradigm by prioritizing webpages based on their influence on the pretraining of LLMs, rather than traditional graph-connectivity metrics like PageRank or indegree. In particular, the proposed method implements a scoring function that evaluates the pretraining influence of web documents using a pretraining influence scorer, derived from data-filtering capabilities similar to those reported in related studies [1] [2].

The paper details the Craw4LLM algorithm which operates through an iterative process of assigning scores to newly discovered web pages, integrating these scores into a priority queue, and selecting the top-rated pages for further exploration and pretraining dataset inclusion. This process stops when a preset number of documents has been crawled.

Experimental Evaluation

The evaluation is conducted using the ClueWeb22 dataset, which consists of 900 million English webpages. The experimental setup involved a direct comparison between Craw4LLM and baseline crawling methods that rely on graph connectivity metrics.

Key findings include the superior performance of Craw4LLM, which reaches comparable LLM pretraining usefulness with only 21% of the URLs that would be required by traditional methods, thus significantly decreasing crawling overhead and computational waste. Additionally, the results showed that Craw4LLM achieves 95% of the performance of an oracle approach that optimally selects pretraining documents from a well-rated data pool, further highlighting its efficiency.

Results and Analysis

With regards to LLM performance, Craw4LLM exhibits notable improvements across various downstream tasks—including commonsense reasoning, language understanding, and symbolic problem solving—demonstrating not just efficiency, but also enhanced model quality. This performance is attributable to Craw4LLM's ability to detect content that aligns well with LLM pretraining needs.

The analysis also presents evidence of significant correlations between pretraining influence scores over connection hops in the web graph, validating the choice of high-scoring documents as entry points for further exploration. Moreover, the scoring method employed shows a clear advantage over traditional indegree-based approaches, which lack correlation with pretraining quality.

Implications

The research posits significant implications for both practical applications and theoretical advancements in efficient LLM pretraining. Practically, Craw4LLM reduces the volume of unnecessary data crawled and processed, alleviating server burden and ethical concerns associated with over-crawling. Theoretically, this approach opens new avenues for refining crawling strategies based on specific pretraining objectives, rather than relying on generic web metrics.

Future Directions

Future research could explore integrating Craw4LLM with real-time crawling engines beyond simulations, addressing the complexities of dynamic web environments. Furthermore, while this paper mitigates some challenges of data acquisition, it acknowledges ongoing ethical debates surrounding fair use and recommends comprehensive strategies for compliant and sustainable web data usage.

In summary, Craw4LLM represents an incremental innovation in the domain of data acquisition for LLMs, providing an efficient alternative to traditional web crawling schemes that prioritize connectivity over content relevance. As the demand for high-quality pretraining data grows, developments like Craw4LLM are crucial in ensuring that the process remains both effective and responsible.

References:

1. Jeffrey Li et al. "DataComp-LM: In search of the next generation of training sets for LLMs," NeurIPS, 2024.
2. Guilherme Penedo et al. "The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale," NeurIPS, 2024.