• GPU parallel computing revolution
• Cloud computing lowering barriers
• Specialized AI chips emerging

• Internet content exponential growth
• Accelerated digitization process
• Mature data annotation techniques

• Transformer revolutionary breakthrough
• Attention mechanism innovation
• End-to-end learning paradigm

• Generative Pre-trained Transformer: Large language model based on Transformer architecture
• Autoregressive language model: Generates text by predicting the next word
• Unsupervised pre-training + Supervised fine-tuning: Typical representative of two-stage training paradigm

• Unidirectional attention mechanism: Can only see previous text, suitable for text generation tasks
• Large-scale parameters: From GPT-1's 117 million to GPT-4's hundreds of billions of parameters
• Powerful zero-shot and few-shot learning capabilities

• Multi-head self-attention: Captures long-range dependencies
• Positional encoding: Understands positional information in text sequences
• Residual connections + Layer normalization: Stabilizes training process

• Next word prediction: Learning language patterns from large-scale text corpora
• Causal masking: Ensures access only to previous words
• Large-scale data: Diverse data sources including internet text, books, news

• Instruction fine-tuning: Improves model's ability to follow instructions
• Reinforcement Learning from Human Feedback (RLHF): Aligns model outputs with human preferences
• Safety filtering: Reduces generation of harmful content

• Creative writing: Stories, poetry, script creation
• Technical documentation: API docs, user manuals, technical reports
• Marketing content: Ad copy, product descriptions, social media content

• Reading comprehension: Answering complex text-based questions
• Logical reasoning: Solving mathematical problems and logic puzzles
• Knowledge Q&A: Cross-domain encyclopedic knowledge queries

• Program writing: Generating code based on requirements
• Code explanation: Understanding and commenting existing code
• Debugging assistance: Finding and fixing code errors

• Strong generalization: One model handles multiple tasks
• In-context learning: Quickly adapts to new tasks through examples
• Creative output: Generates novel and useful content

• Hallucination issues: May generate seemingly reasonable but actually incorrect information
• Knowledge cutoff: Training data has time limitations
• High computational cost: Inference requires significant computational resources
• Poor interpretability: Difficult to understand model's decision-making process

Definition: Token is the smallest unit for model text processing, can be a single character, word, or even part of a word. Created by a tokenizer that splits raw text.

⌨️ Analogy: If you think of a sentence as a wall built with blocks, tokens are each individual block.

Definition: Dimension is the position or "length" of each data point in a vector or matrix. Commonly used to describe the vector space size in hidden layers.

📦 Analogy: If a token is a product, dimensions are the number of "feature labels" it has, like color, size, purpose, etc.

Definition: Parameters are the "knowledge" learned by the model during training. They include weights and biases in neural networks.

Scale: GPT-3 has 175 billion parameters, GPT-4 is estimated to have even more.

🧠 Analogy: Think of the model as a brain, parameters are the "memory connections" or "experiences" formed in that brain.

Learning input-output mappings from labeled data

Examples: Image classification, sentiment analysis

Optimizing strategies through trial and error with reward signals

Examples: Game AI, robot control

Multi-layer neural networks automatically extracting features

End-to-end learning of complex patterns