CONCEPT

Large Language Models

Large Language Models (LLMs) are deep neural network models with billions to trillions of parameters trained on vast text corpora using self-attention mechanisms to perform natural language understanding, generation, and complex reasoning.

Base Architecture: Transformer (Multi-Head Self-Attention)
Training Pipeline: Unsupervised Pre-training → SFT (Supervised Fine-Tuning) → RLHF
Representative Models: GPT-4, Claude 3.5, Gemini 1.5, LLaMA 3, Mistral Large

CONCEPTLarge Language Models

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Quick facts

At a glance

Architecture: Decoder-only / Encoder-Decoder Transformer
Training Objective: Autoregressive Next-Token Prediction
Scale: 7B to 1.8T+ parameters
Core Capabilities: In-context learning, few-shot prompting, code synthesis, reasoning
Key Alignment Technique: Reinforcement Learning from Human Feedback (RLHF) / DPO

How Large Language Models Work

Mechanism

At their core, Large Language Models are statistical next-token prediction systems. Text is broken down into sub-word tokens and mapped into high-dimensional vector spaces (embeddings). Through dozens of stacked Transformer layers, the self-attention mechanism computes pairwise relationship weights between every token in the context window simultaneously.

By training across trillions of tokens of diverse literature, code, and technical discourse, the model develops an internal world model capable of grammatical mastery, algorithmic reasoning, and multi-step problem solving without task-specific engineering.

The Three Stages of Modern LLM Training

Training Stages

1. Pre-training: Massive self-supervised next-token prediction across trillions of tokens on thousands of GPUs, imparting base knowledge and reasoning abilities.
2. Supervised Fine-Tuning (SFT): Instruction tuning on curated high-quality demonstration dialogues to teach the model to act as an assistant and follow user prompts.
3. Alignment (RLHF / DPO): Reinforcement Learning from Human Feedback or Direct Preference Optimization to steer model outputs toward helpfulness, accuracy, and safety.