chapter two

2 Introduction to large language models

 

This chapter covers

  • Overview of Large Language Models (LLMs)
  • Key use cases powered by LLMs
  • Foundational Models and their impact on AI Development
  • New architecture concepts for LLMs - prompts, prompt engineering, embeddings, tokens, model parameters, context window, and emergent behavior
  • Comparison of open-source and commercial LLMs

Large Language Models (LLMs) are Generative AI models that can understand and generate human-like text based on a given input. LLMs are the foundation for many natural language processing (NLP) tasks, such as search, speech-to-text, sentiment analysis, text summarization, and more. LLMs are general-purpose language models that are pre-trained and can be fine-tuned for specific tasks and purposes.

This chapter explores the fascinating world of Large Language Models (LLMs) and their transformative impact on artificial intelligence. As a significant advancement in AI, LLMs have demonstrated remarkable capabilities in understanding and generating human-like text, enabling various applications across various industries. We dive into the critical use cases of LLMs, the different types of LLMs, and the concept of foundational models that have revolutionized AI development.

2.1 Overview of Foundational Models

2.2 Overview of LLMs

2.3 Transformer Architecture

2.3.1 Training Cut-Off

2.4 Types of LLMs

2.5 Open-Source vs. Commercial LLMs

2.5.1 Commercial LLMs

2.5.2 Open Source LLMs

2.6 Key Concepts of LLMs

2.6.1 Prompts

2.6.2 Tokens

2.6.3 Embeddings

2.6.4 Model configuration

2.6.5 Context Window

2.6.6 Prompt Engineering

2.6.7 Model adaptation

2.6.8 Emergent Behavior

2.7 Summary

2.8 References