Cleaner Pretraining Corpus Curation with Neural Web Scraping (2402.14652v3)

Abstract: The web contains large-scale, diverse, and abundant information to satisfy the information-seeking needs of humans. Through meticulous data collection, preprocessing, and curation, webpages can be used as a fundamental data resource for LLM pretraining. However, when confronted with the progressively revolutionized and intricate nature of webpages, rule-based/feature-based web scrapers are becoming increasingly inadequate. This paper presents a simple, fast, and effective Neural web Scraper (NeuScraper) to help extract primary and clean text contents from webpages. Experimental results show that NeuScraper surpasses the baseline scrapers by achieving more than a 20% improvement, demonstrating its potential in extracting higher-quality data to facilitate the LLM pretraining. All of the code is available at https://github.com/OpenMatch/NeuScraper.

• The paper introduces NeuScraper, a neural web scraping method that improves text extraction quality by over 20% compared to traditional approaches.
• It employs a shallow neural architecture combined with DOM layout analysis and XLMRoberta text representation to achieve precise content extraction.
• Empirical validation shows that NeuScraper enhances downstream LLM performance and reduces perplexity on benchmarks like Wikitext and Lambada.

Enhancing Pretraining Corpus Quality with Neural Web Scraping: Insights from NeuScraper

The Imperative for High-Quality Text Extraction

With the ascendancy of LLMs across a plethora of NLP tasks, the imperative for high-quality, large-scale datasets has never been more critical. The proportional scaling of model size and training data posits a unique challenge of sourcing and preparing suitable datasets for pretraining purposes. Common web-crawled datasets, despite their ubiquity and scale, often fall short in quality due to the presence of irrelevant content such as advertisements and hyperlinks, underscoring the need for sophisticated web scraping techniques.

Current Web Scraping Paradigms

Current methodologies predominantly leverage rule-based or feature-based web scrapers to sift through the chaotic heterogeneity of web pages. These approaches, while foundational, exhibit limitations in adaptability and require significant manual intervention to maintain, making them less viable with the increasing complexity of web pages. This backdrop sets the stage for exploring more dynamic, less labor-intensive solutions that can extract primary content with higher precision and efficiency.

Introducing NeuScraper

This context brings us to the advent of NeuScraper, a novel approach grounded in neural networks to address the challenges of web scraping for pretraining corpora. NeuScraper eschews traditional rule or feature-based methods for a shallow neural architecture combined with layout information for parsing, presenting a significant leap in efficacy, as evidenced by a more than 20% improvement in text extraction quality over conventional baseline scrapers.

Leveraging Neural Architectures for Web Scraping

NeuScraper's methodology encapsulates a compelling fusion of sequence modeling of web pages informed by DOM tree structures, and a hierarchical architecture for node-level prediction, utilizing the XLMRoberta model for text representation. This innovative approach enables NeuScraper to discern and extract primary content with notable precision, evidenced by a substantial boost in performance metrics such as accuracy, precision, recall, and F1 scores, alongside notable reductions in latency when deployed on GPU infrastructure.

Empirical Validation and Implications

The empirical validation of NeuScraper presents compelling results. When benchmarked against a suite of conventional web scrapers across the ClueWeb22 dataset, NeuScraper's superiority emerges not just in the immediate context of text extraction effectiveness but also in the downstream impact on the quality of pretraining corpora for LLMs. LLMs pretrained on data curated by NeuScraper showcased enhanced performance in standard NLP tasks, validating the hypothesis that the quality of pretraining data is a pivotal determinant of model efficacy.

Moreover, the reduced perplexity in reproducing target corpora such as Wikitext and Lambada further underscores the qualitative advantage conferred by NeuScraper's neural approach to web scraping. This result is particularly indicative of the potential for neural web scrapers to elevate the baseline for dataset preparation in the NLP domain.

Future Trajectories and Considerations

The exploration of neural web scraping, as epitomized by NeuScraper, opens new vistas in the curation of pretraining corpora. The demonstrated effectiveness and efficiency of neural approaches in navigating the complexities of web content extraction herald a promising avenue for advancing the capabilities of LLMs. However, this venture is not without its exigencies. The reliance on GPU parallelism for high-speed scraping and the need for high-throughput storage mediums for large-scale corpus processing delineate the infrastructure prerequisites for leveraging neural web scraping at scale.

Conclusion

In summation, NeuScraper's contribution to the corpus curation process for LLM pretraining is illustrative of the broader potential of neural methods in web scraping. By addressing the limitations of existing rule-based and feature-based approaches, NeuScraper not only showcases the immediate benefits in terms of data quality and scraping efficiency but also sets a new benchmark for future explorations in the field. As we venture further into the development of more sophisticated LLMs, the role of advanced web scraping methods like NeuScraper will undoubtedly become increasingly central.