Transformer-Based Large Language Models for Context-Aware Semantic Understanding and Domain-Specific Text Generation

Abstract

Transformer-based large language models (LLMs) have reshaped the field of natural language processing by facilitating high-quality text generation and advanced contextual semantic understanding in a wide variety of applications; nonetheless, the generation of scientifically valid and context-aware domain-specific content is one of the biggest challenges because of semantic ambiguity, contextual inconsistency, and constraints of knowledge representation in a specialized form. This paper presents a framework of an encoder-transformer structure of context-sensitive semantic interpretation and text generation in domain using scientific literature corpora, gathered in publicly accessible archives like arXiv and PubMed. The posteriori framework is based on encoder transformer architecture to learn deep contextual representations, semantic representations, and domain-adaptive linguistic representations to enhance scientific text generation. Scientific article datasets train and fine-tune the model, and are assessed with several semantic understanding and text generation metrics, such as Accuracy, F1-Score, BLEU, ROUGE, METEOR, BERTScore, and Perplexity. The experimental findings indicate that the suggested framework performs better compared to traditional methods of LSTM, Bi-LSTM, GPT-2 and standard transformer frameworks in terms of preserving context, semantic coherence, technical consistency, and the quality of generated texts and the perplexity and linguistic fluency decreases. The suggested solution also helps build intelligent language scientific processing systems that can be used in the context of automated academic writing, technical summarization, semantic knowledge extraction, and other domain-specific applications of NLP.