New AI framework uses multimodal large language models to automatically scrape data from complex, interactive websites

Webscraper leverages Multimodal Large Language Models (MLLMs) to autonomously navigate dynamic websites that traditional static HTML parsers cannot handle

Framework employs a structured five-stage prompting procedure combined with custom-built tools for reliable data extraction from index-and-content website architectures

Tested on six news websites, the full Webscraper system demonstrates significant improvements in extraction accuracy compared to conventional web scraping methods

Addresses limitations of brittle, manually-customized scrapers by enabling AI-driven navigation of interactive interfaces and structured data extraction