Emergence of LLMs: Evolution and Challenges

Author(s)	Sony Snigdha Sahoo
Country	India
Abstract	Language is central to human cognition and communication, making its computational modeling a fundamental objective of Artificial Intelligence(AI). This paper presents a comprehensive study of the evolution of Natural Language Processing (NLP), tracing its trajectory from rule-based and statistical approaches to neural architectures and modern Large Language Models (LLMs). The limitations of early symbolic and probabilistic systems have been analyzed and the transformative role of neural networks and distributed representations has been highlighted. The introduction of the Transformer architecture, with its self-attention mechanism, is identified as a pivotal breakthrough enabling scalable and context-aware language modeling. This paper further examines the computational foundations of LLMs, including probabilistic sequence modeling, tokenization, embeddings, and attention mechanisms and provides a unified understanding of LLMs and their implications for the future of AI.
Keywords	Natural Language Processing, Large language models, Transformer architecture, Self-attention, Tokenization
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 4, July-August 2025
Published On	2025-07-10
DOI	https://doi.org/10.36948/ijfmr.2025.v07i04.79774

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