Deep Dive into Large Language Models
This course provides a comprehensive and in-depth introduction to the development history of large language models (LLMs), their core technical architectures, training paradigms (pretraining, fine-tuning, and alignment), multimodal extensions, prompt engineering, chain-of-thought reasoning, agents, as well as frontier topics such as model safety and privacy protection.
Course Overview
Content Summary
This course provides a comprehensive and in-depth introduction to the evolution of Large Language Models (LLMs), core technical architectures, training paradigms (pre-training, fine-tuning, and alignment), multimodal extensions, prompt engineering, Chain of Thought (CoT), agents, as well as frontier topics such as model safety and privacy protection.
Deep analysis of the technological evolution and safety alignment of full-stack large models, from pre-training to general agents.
Learning Objectives
- Distinguish between model architectures: Identify the structural differences and use cases for Encoder-only, Decoder-only, and Encoder-Decoder models.
- Explain the LLM Training Pipeline: Describe the transition from self-supervised pre-training to Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF).
- Analyze Model Scaling and Behavior: Explain the concepts of Scaling Laws, Emergent Abilities (In-context learning, Chain of Thought), and the phenomenon of Hallucinations.
- Analyze the structural differences between Encoder-only (BERT), Decoder-only (GPT), and Encoder-Decoder (T5) architectures.
- Explain the three-stage training process: Pre-training (Base model), Instruction Tuning (SFT), and Alignment (RLHF/PPO).
- Compare the performance, scaling laws, and architectural innovations of mainstream LLMs including GPT, Llama, Qwen, and DeepSeek.
- Implement zero-shot and few-shot prompting strategies for structured data extraction and classification.
- Calibrate model hyperparameters (Temperature, Top P, Penalties) to balance creative and deterministic outputs.
- Construct effective Chain-of-Thought (CoT) prompts using manual, automatic, and zero-shot ("Let's think step by step") methods.
- Analyze and Compare CoT Variants: Differentiate between Self-Consistency, Program of Thought (PoT), Tree-of-Thought (ToT), and Graph-of-Thought (GoT) architectures.