Introduction to Large Language Models
Chapter 1: Understanding Large Language Models
What is Artificial Intelligence? A Brief Overview
Introducing Natural Language Processing (NLP)
Defining Large Language Models (LLMs)
How LLMs Learn from Text Data
Examples of Tasks LLMs Can Perform
Common Misconceptions about LLMs
Quiz for Chapter 1
Chapter 2: The Mechanics of LLMs (Simplified)
Representing Words: Tokens and Embeddings
Predicting the Next Word: The Core Idea
The Role of Training Data Size
Understanding Model Parameters
Introduction to Transformer Architecture (High-Level)
How Context Influences Generation
Quiz for Chapter 2
Chapter 3: Communicating with LLMs: Prompts
What is a Prompt?
Basic Prompting Techniques
Providing Instructions Clearly
Giving Examples (Few-Shot Prompting)
Controlling Output Length and Format
Common Prompting Challenges
Practice: Crafting Your First Prompts
Quiz for Chapter 3
Chapter 4: A Look at Different LLMs
Overview of Foundational Models
General Purpose vs. Specialized Models
Open vs. Closed Models: What's the Difference?
Understanding Model Size and Capabilities
Accessing Models: APIs and Interfaces
Quiz for Chapter 4
Chapter 5: Using Pre-trained LLMs
What are Pre-trained Models?
Finding and Choosing an LLM Service
Interacting via Web Interfaces
Introduction to Using LLM APIs
Sending Your First API Request
Interpreting LLM Responses
Hands-on Practical: Simple Text Generation Task
Quiz for Chapter 5
Overview of Foundational Models
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- A Survey of Large Language Models, Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, Ji-Rong Wen, 2023 arXiv preprint arXiv:2303.18223 DOI: 10.48550/arXiv.2303.18223 - Provides a comprehensive review of large language models, including their development, architectures, training methodologies (pre-training on vast data), and adaptation techniques, directly supporting the understanding of foundational models.
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova, 2018 Proceedings of NAACL-HLT 2019 DOI: 10.48550/arXiv.1810.04805 - This paper introduced the BERT model, which established the highly influential paradigm of pre-training a large general-purpose language model on extensive data and then fine-tuning it for various downstream tasks, a core concept for foundational models.
- CS224N: Natural Language Processing with Deep Learning, Diyi Yang, Tatsunori Hashimoto, 2023 (Stanford University) - This university course provides in-depth lectures and materials on the principles of natural language processing with deep learning, covering topics such as large language model architectures, pre-training, and fine-tuning, which are fundamental to understanding foundational models.