Privacy and Security for Large Language Models

Chapter 2. Understanding Large Language Models

In recent years, large language models (LLMs) have emerged as a groundbreaking technology in the field of natural language processing (NLP). These powerful models have revolutionized the way machines understand, generate, and manipulate human language, enabling a wide range of applications such as language translation, text summarization, question answering, and content creation. In this chapter, you will explore the fundamentals of LLMs, delving into their architectures, pre-training techniques, evaluation metrics, and the privacy and security assessment associated with their development and deployment.

Fundamentals of Large Language Models

LLMs are a class of deep learning models designed to process and generate human language. They are usually trained on vast amounts of text data, allowing them to learn the intricacies and patterns of language at an unprecedented scale. LLMs have the ability to capture semantic meaning, grammatical structure, and contextual nuances of text, making them highly effective in a wide range of NLP tasks.

Basic Building Blocks of Language Models

First, we will cover the basic building blocks of language models, from the microscopic level to the macroscopic level. Experienced readers can choose to skip some levels if desired. We will cover some of the levels in more detail in later chapters, such as fine-tuning and reinforcement learning from human feedback.

Neural networks

At the core of language models ...