基于 BERT 的多阶段文档排序

Search technology focuses on rapidly retrieving relevant information. Novel approaches leverage the BERT language model for intelligent document evaluation, enhancing search efficiency through a multi-stage processing pipeline. This system initially conducts a swift preliminary scan, discarding low-relevance results, and subsequently performs in-depth analysis on high-potential documents. This architecture allows for a flexible trade-off between search quality and response latency. One model assesses the relevance of individual documents, while another compares document pairs to determine their relative importance. This collaborative mechanism enables the system to be fine-tuned for either faster response times or higher result accuracy, depending on specific requirements. Extensive testing on large datasets demonstrates that this method significantly improves the discovery of useful answers while maintaining operational speed, ultimately providing users with a smarter and more efficient search experience. The multi-stage design ensures that computational resources are primarily allocated to the most promising candidates, optimizing overall performance.