In recent years, learning machines, particularly large language models (LLMs) like ChatGPT, have revolutionized natural language processing (NLP). This article provides an overview of how such models function, focusing on the underlying architecture, training methodology, and inference mechanisms. It also explores their applications, limitations, and ethical concerns. Drawing on insights from computer science and cognitive modeling, this work explains how machines "learn" to understand and generate human-like language.

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