CROSS-ENCODER MODELS FOR ENHANCED SEARCH RELEVANCE: A MULTI-DOMAIN ANALYSIS OF PERFORMANCE AND APPLICATIONS

Abstract

Cross-encoder models have emerged as a transformative approach in search relevance ranking, offering significant improvements over traditional feature-based methods. This article presents a comprehensive analysis of recent advances in cross-encoder architectures and their applications across diverse domains. The article examines key innovations in model design, including dynamic negative sampling techniques, knowledge distillation for latency optimization, and hybrid architectures that combine cross-encoders with bi-encoders. The article explores specialized implementations in scientific literature databases, legal information systems, medical document retrieval, technical documentation platforms, and educational resource matching. The analysis demonstrates consistent improvements in relevance metrics compared to conventional ranking methods. The present novel approaches to handling longer documents through hierarchical attention mechanisms and efficient token pruning strategies. The article also addresses challenges in scaling these models and proposes solutions for domain-specific adaptations. The findings indicate that cross-encoder models represent a significant step forward in search technology, particularly in their ability to capture subtle relevance signals and contextual relationships between queries and documents.