Adaptive Semantic Layering for Multilevel Contextual Precision in Large Language Models
Nathan Aqadah, Tobias Haverford, Edward Walker, Xavier Blackwood, Ronald Hall, Sullivan Merriweather · 2024
The increasing complexity of language tasks necessitates models capable of complex contextual understanding across multiple levels. Addressing this need, Adaptive Semantic Layering (ASL) introduces a hierarchical framework that structures semantic information within Large Language Models (LLMs), thereby enhancing their ability to process intricate linguistic patterns. Through the integration of ASL, LLMs exhibit improved performance in domain-specific terminology comprehension, response time efficiency, and adaptability to linguistic variations. Empirical evaluations demonstrate that ASL effectively addresses existing limitations in LLM architectures, offering a novel approach that contributes to the theoretical and practical advancement of natural language processing technologies.