We introduce a state-of-the-art approach for URL categorization that
leverages the power of Large Language Models (LLMs) to address the primary
objectives of web content filtering: safeguarding organizations from legal and
ethical risks, limiting access to high-risk or suspicious websites, and
fostering a secure and professional work environment. Our method utilizes LLMs
to generate accurate classifications and then employs established knowledge
distillation techniques to create smaller, more specialized student models
tailored for web content filtering. Distillation results in a student model
with a 9\% accuracy rate improvement in classifying websites, sourced from
customer telemetry data collected by a large security vendor, into 30 distinct
content categories based on their URLs, surpassing the current state-of-the-art
approach. Our student model matches the performance of the teacher LLM with 175
times less parameters, allowing the model to be used for in-line scanning of
large volumes of URLs, and requires 3 orders of magnitude less manually labeled
training data than the current state-of-the-art approach. Depending on the
specific use case, the output generated by our approach can either be directly
returned or employed as a pre-filter for more resource-intensive operations
involving website images or HTML.

本研究利用大型语言模型进行 URL 分类，并通过知识蒸馏技术生成了精简版学生模型，使得模型在以 URL 为基础的网页分类任务中准确度提高了 9%，比此前状态 - of-the-art 的方法表现更佳，精简版学生模型使得模型可用于大规模 URL 的内联扫描，并且所需的手工标记训练数据量较之前的方法减少了 3 个数量级。