Understanding Large Language Models: Architecture, Training, and Capabilities
Large Language Models (LLMs) have revolutionized how we interact with AI. Understanding their inner workings is essential for effective prompt engineering and AI application development.
Transformer Architecture
At the heart of modern LLMs lies the Transformer architecture:
Self-Attention Mechanism
The key innovation that allows models to understand context and relationships between words:
Architecture Components
1. **Encoder-Decoder vs Decoder-Only**
- GPT series: Decoder-only (autoregressive)
- BERT: Encoder-only (bidirectional)
- T5: Encoder-Decoder (seq2seq)
2. **Layer Structure**
- Multi-head self-attention
- Feed-forward networks
- Residual connections
- Layer normalization
Training Process
Pre-training
The foundation of LLM capabilities:
Fine-tuning Approaches
1. **Supervised Fine-tuning (SFT)**
- Task-specific training on labeled data
- Instruction following capabilities
2. **Reinforcement Learning from Human Feedback (RLHF)**
- Aligning models with human preferences
- Improving safety and helpfulness
3. **Parameter-Efficient Fine-tuning**
- LoRA (Low-Rank Adaptation)
- Prefix tuning
- Adapter methods
Key Capabilities
Emergent Abilities
Capabilities that appear at scale:
Limitations and Challenges
Model Scaling Laws
Understanding how performance improves with scale:
Practical Implications
For Prompt Engineers
For Developers
Recent Advances
Understanding these fundamentals enables more effective use of LLMs and better prediction of their behavior in various applications.