Natural Language Processing in the Era of Large Language Models: Foundations, Integration, and Low-Resource Frontiers

Large Language Models (LLMs) have fundamentally transformed the landscape of Natural Language Processing (NLP), subsuming and redefining tasks that were once addressed by specialized, modular pipelines. This paper surveys the role of classical and contemporary NLP within modern LLM architectures, examining how foundational techniques — tokenization, syntactic parsing, semantic representation, and discourse modeling — have been absorbed into, and continue to inform, the pre-training and fine-tuning paradigms of transformer-based models. We further investigate the critical challenge of linguistic inclusivity, focusing on low-resource and morphologically complex languages that remain underserved by dominant English-centric corpora. Drawing on recent advances in cross-lingual transfer learning, multilingual pre-training, and data augmentation, we assess the progress and persistent gaps in extending LLM capabilities to such languages. Case studies on Southeast Asian, African, and indigenous language NLP toolkits illustrate practical strategies and remaining bottlenecks. We conclude by outlining open research directions at the intersection of structural NLP and generative AI.