The LLM Revolution: Understanding Large Language Models
The Reader's Dilemma
Dear Marilyn,Everyone keeps talking about LLMs and GPT, but I'm confused about what makes them different from the machine learning models I learned about in school. Are they just bigger neural networks, or is there something fundamentally different?
Marilyn's Reply
The revolution isn't just about size—it's about emergent capabilities. When you scale neural networks to billions of parameters and train them on vast text corpora, something magical happens: they develop abilities nobody explicitly programmed. Let me explain why this changes everything.
The Spark: Understanding LLMs
What Makes LLMs Different?
Traditional ML models are trained for specific tasks—spam detection, image classification, sentiment analysis. LLMs are trained on a single task: predict the next word. But this simple objective, at scale, produces remarkable generalization.
| Aspect | Traditional ML | Large Language Models |
|---|---|---|
| Training Objective | Task-specific (classification, regression) | Next token prediction |
| Capabilities | Single task | Multi-task, emergent abilities |
| Adaptation | Requires retraining | In-context learning (prompting) |
| Parameters | Thousands to millions | Billions to trillions |
Quick Check
What is the primary training objective of Large Language Models?
The Transformer Architecture
The breakthrough enabling LLMs was the Transformer architecture, introduced in the 2017 paper "Attention Is All You Need." The key innovation: self-attention.
Self-Attention Explained
Self-attention allows each word to "look at" every other word in the input and decide how much to pay attention to it.
"The cat sat on the mat because it was tired."
Self-attention helps the model understand that "it" refers to "cat," not "mat."
Quick Check
What problem does self-attention solve in language understanding?
Emergent Capabilities
Perhaps the most fascinating aspect of LLMs is emergent capabilities—abilities that appear suddenly at certain scales without being explicitly trained.
In-Context Learning
LLMs can learn new tasks from just a few examples in the prompt, without any weight updates. This wasn't programmed—it emerged.
Chain-of-Thought Reasoning
When prompted to "think step by step," LLMs can solve complex reasoning problems they otherwise couldn't. This capability emerged at around 100B parameters.
Code Generation
Models trained on text that included code learned to write functional programs, even though coding wasn't a specific training objective.
Quick Check
What is 'in-context learning' in LLMs?
Key LLM Families
| Model Family | Creator | Key Features |
|---|---|---|
| GPT-4 | OpenAI | Multimodal, strong reasoning |
| Claude | Anthropic | Constitutional AI, long context |
| Llama | Meta | Open weights, efficient |
| Gemini | Native multimodal | |
| Mistral | Mistral AI | Efficient, open source |
Quick Check
Which LLM family is known for being open-weights and developed by Meta?