Transformer-Based Large Language Models for Context-Aware Semantic Computing Applications

Abstract

Transformer-based Large Language Models (LLMs) have significantly transformed the field of natural language processing and context-aware semantic computing by enabling machines to understand, generate, and reason over human language with high contextual fidelity. These models, including architectures such as BERT, GPT, and T5, leverage self-attention mechanisms to capture long-range dependencies and semantic relationships across large textual corpora. As a result, they have become foundational in applications such as intelligent question answering, semantic search, conversational agents, code generation, healthcare analytics, and knowledge graph construction. Despite their remarkable performance, challenges remain in deploying transformer-based LLMs for context-aware semantic computing applications, particularly in terms of computational cost, interpretability, domain adaptation, hallucination control, and real-time scalability. To address these issues, this research proposes a Transformer-Based Large Language Model Framework for Context-Aware Semantic Computing Systems, designed to enhance semantic understanding, contextual reasoning, and adaptive intelligence across diverse application domains. The proposed framework integrates transformer encoders, retrieval-augmented generation (RAG), attention-based contextual alignment, and semantic embedding fusion to improve accuracy and contextual consistency. Additionally, the system incorporates explainability modules and confidence-aware inference mechanisms to improve trustworthiness in semantic decision-making tasks. Experimental evaluation demonstrates that transformer-based semantic computing systems significantly outperform traditional NLP models in terms of contextual accuracy, semantic relevance, and reasoning capability. The results confirm that integrating retrieval mechanisms and explainability layers enhances both performance and interpretability in large-scale semantic computing applications.