Abstract

This paper explores the evolution of Large Language Models (LLMs) over the past 30 years, tracing their development from statistical language models in the 1990s to the sophisticated multimodal and reasoning models of the 2020s. It highlights key milestones like the introduction of neural language models, word embeddings, the Transformer architecture, and the rise of models like BERT, the GPT series, and ChatGPT. The paper also examines the challenges and opportunities that have shaped LLM development, including computational resource demands, ethical considerations, and the need for explainability and trust. It concludes by emphasizing the shift towards efficiency, specialized capabilities, and responsible development as key factors in shaping the future of LLMs.