07-24-2026, 08:03 PM
Natural Language Processing and Large Language Models
(free chapter)
Summary
This chapter provides an accessible introduction to Natural Language Processing (NLP) and Large Language Models (LLMs), explaining how computers learn to understand, interpret, and generate human language. It traces the evolution of NLP from traditional rule-based and statistical approaches to modern transformer-based architectures that power systems such as ChatGPT. The authors describe how LLMs are trained on massive text datasets using self-supervised learning, enabling them to perform a wide variety of language tasks—including translation, summarization, question answering, text generation, and information extraction—without requiring task-specific programming.
The chapter also discusses the strengths of LLMs, such as their flexibility, scalability, and impressive performance across domains, while acknowledging important limitations including hallucinations, embedded biases, high computational costs, privacy concerns, and the lack of true reasoning or factual understanding. Particular attention is given to the growing role of LLMs in education and scientific research, where they can support learning, writing, coding, and data analysis when used responsibly. The authors conclude that although LLMs are transforming the field of NLP and creating new opportunities for human–AI collaboration, their outputs should always be critically evaluated and complemented by human expertise, ethical awareness, and domain knowledge.
CHAPTER
(free chapter)
Summary
This chapter provides an accessible introduction to Natural Language Processing (NLP) and Large Language Models (LLMs), explaining how computers learn to understand, interpret, and generate human language. It traces the evolution of NLP from traditional rule-based and statistical approaches to modern transformer-based architectures that power systems such as ChatGPT. The authors describe how LLMs are trained on massive text datasets using self-supervised learning, enabling them to perform a wide variety of language tasks—including translation, summarization, question answering, text generation, and information extraction—without requiring task-specific programming.
The chapter also discusses the strengths of LLMs, such as their flexibility, scalability, and impressive performance across domains, while acknowledging important limitations including hallucinations, embedded biases, high computational costs, privacy concerns, and the lack of true reasoning or factual understanding. Particular attention is given to the growing role of LLMs in education and scientific research, where they can support learning, writing, coding, and data analysis when used responsibly. The authors conclude that although LLMs are transforming the field of NLP and creating new opportunities for human–AI collaboration, their outputs should always be critically evaluated and complemented by human expertise, ethical awareness, and domain knowledge.
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