# Web Page Classification using LLMs for Crawling Support

arXiv: https://arxiv.org/abs/2505.06972 · HTML: https://mlsanity.com/paper/2505.06972/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 11 May 2025.

## Builds on (most cited first, 2 of 2)

- [AutoScraper: A Progressive Understanding Web Agent for Web Scraper Generation](https://mlsanity.com/paper/2404.12753/) (2024)
- [LLMs are One-Shot URL Classifiers and Explainers](https://mlsanity.com/paper/2409.14306/) (2024)

## Built on by (most cited first, 2 of 2)

- [Trust Me, I Know This Function: Hijacking LLM Static Analysis using Bias](https://mlsanity.com/paper/2508.17361/) (2025)
- [Eliciting Least-to-Most Reasoning for Phishing URL Detection](https://mlsanity.com/paper/2601.20270/) (2026)

## Abstract (arXiv)

A web crawler is a system designed to collect web pages, and efficient crawling of new pages requires appropriate algorithms. While website features such as XML sitemaps and the frequency of past page updates provide important clues for accessing new pages, their universal application across diverse conditions is challenging. In this study, we propose a method to efficiently collect new pages by classifying web pages into two types, "Index Pages" and "Content Pages," using a large language model (LLM), and leveraging the classification results to select index pages as starting points for accessing new pages. We construct a dataset with automatically annotated web page types and evaluate our approach from two perspectives: the page type classification performance and coverage of new pages. Experimental results demonstrate that the LLM-based method outperformed baseline methods in both evaluation metrics.