How do LLMs models work?

A Large Language Model (LLM) is a highly complex neural network, predominantly based on the Transformer architecture. It is not a reasoning machine but a sophisticated statistical prediction engine trained to estimate the probability of the next token (word or sub-word unit) in a sequence. This probabilistic approach allows it to generate coherent, fluent, and contextually relevant text.

Core Architecture: The Transformer

The efficiency and performance of modern LLMs are rooted in the Transformer architecture.

• Self-Attention Mechanism: This mechanism is the key innovation. It enables the model to process the entire input sequence in parallel, unlike older models that processed text sequentially.
  • It calculates and weights the relevance of every other token to the current token being processed, allowing the model to effectively capture long-range dependencies and context (e.g., resolving ambiguous pronouns). The Transformer uses self-attention to look at all the words in the input sequence simultaneously. This mechanism allows the model to weigh the relevance of every other word to the current word it's processing. For example, in the sentence "The animal didn't cross the road because it was too wide," attention helps the model know that "it" refers to "road," not "animal.
• Parallel Processing: Because the self-attention mechanism processes the entire input sequence at once, the Transformer can be trained much more efficiently on modern hardware.
• Scalability: The parallel nature of the attention mechanism makes the training of massive models (with billions or trillions of parameters) feasible on modern computing hardware.

The Process: Training and Inference

The operation of an LLM can be broken down into three high-level stages: Pre-training, Fine-tuning/Alignment, and Inference (Usage).

1. Pre-training: The Statistical Engine

This is where the model learns the core rules of language, grammar, and a massive amount of world knowledge.

• Tokenization: First, the raw text data is converted into numerical, machine-readable units called tokens (sub-words or characters). The word "tokenization" might be split into tokens like token, izer, and nation.
• Word Embeddings: Each token is converted into a vector (a list of numbers) that represents its meaning and context. Words with similar meanings (e.g., "king" and "queen") have vectors that are numerically closer in this multi-dimensional space.
• Self-Supervised Learning: The model is trained on a vast corpus of text (trillions of words from the internet, books, etc.) to perform a basic task: predicting the next token in a sentence. It does this by continually adjusting its internal parameters (the "weights" and "biases"—often billions or trillions of them) to minimize the difference between its prediction and the actual next token. This predictive ability is what gives the LLM its coherence and vast knowledge.

2. Fine-tuning and Alignment

After pre-training, the LLM is a powerful predictor but may not be good at following instructions or being helpful and harmless. This stage adjusts the model's behavior.

• Instruction Tuning: The model is fine-tuned on a smaller dataset of high-quality prompt-response pairs (e.g., User prompt: "Explain X," Model response: "Explanation Y"). This teaches the model to follow explicit instructions.
• RLHF (Reinforcement Learning from Human Feedback):

1. Human evaluators rank multiple model responses to a prompt.
2. A Reward Model is trained to predict these human preferences.
3. The LLM is optimized against the Reward Model to align its outputs with defined human standards and safety guidelines.

3. The Inference Process (usage)

When a user submits a query (inference), the LLM generates a response token-by-token: When a user provides a prompt (a query, inference) (e.g., "What is the capital of France?"), the LLM follows these steps to generate a response:

1. Input Encoding: The prompt is tokenized and converted into numerical vectors.
2. Probability Calculation: The model uses its learned parameters to calculate the probability distribution for every possible next token that could logically follow your input.
3. Token Sampling: The model selects the next token based on this probability distribution (often choosing the most probable, or randomly sampling from the top few to ensure variety).
4. Autoregression/Loop: The newly generated token is added to the input sequence, and the process repeats, predicting the next token, and the next, until it generates a special "stop" token or reaches a maximum length.

The output, "The capital of France is Paris," is simply the most statistically probable sequence of tokens generated based on its training.

Key Takeaway

An LLM doesn't "understand" in the human sense. Instead, it is a massive statistical correlation engine that generates incredibly human-like, coherent, and knowledgeable text by mastering the statistical relationships between tokens across trillions of examples.

How do LLM models work - ChatGPT

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