Large Language Models — How LLMs Actually Work, Explained

Overview

Large language models (LLMs) are AI systems behind chatbots, trained on trillions of tokens to predict the next word, which enables fluent human-like text generation, translation, and coding. Their training involves pre-training for knowledge and fine-tuning with human feedback for helpfulness, all powered by the transformer architecture.

In this video

• A large language model (LLM) fundamentally predicts the next token, where a token is a chunk of text roughly four characters.
• Trained on trillions of tokens, modern LLMs learn the statistical likelihood of which token tends to follow another, developing skills like translation, summarizing, and reasoning.
• LLM training occurs in two stages: pre-training, which builds knowledge by predicting the next token from vast texts, and fine-tuning, which uses reinforcement learning from human feedback (R-L-H-F) to instill helpfulness.
• The transformer architecture, a breakthrough from 2017, utilizes 'attention' to enable every token to consider all other tokens for long-range relationships in parallel.
• Models operate on 'tokens' within a 'context window,' which can range from a few thousand to over 1 million tokens, defining the amount of information the model can process at once.
• Text generation happens one token at a time, with a 'temperature' setting controlling how adventurous or predictable the chosen next token will be.
• LLMs can hallucinate by confidently stating false information because they are trained for plausible text production, not verified truth.
• To address hallucination, grounding LLMs with real sources, connecting them to search tools, and verifying critical output are essential practices.
• Alignment uses human feedback and guardrails to make models helpful, honest, and harmless, preventing them from fulfilling dangerous requests, though biases and jailbreaking remain ongoing challenges.

Frequently asked questions

What is the primary function of a large language model?

A large language model's core function is to predict the next token, which is a chunk of text roughly four characters. This single skill, at a large enough scale, enables tasks such as translation, summarizing, reasoning, and writing code.

How are large language models trained?

LLMs are trained in two stages: pre-training, where they read enormous amounts of text to predict the next token and gain knowledge, followed by fine-tuning, which includes reinforcement learning from human feedback (R-L-H-F) to teach them to be helpful and follow instructions.

What is the transformer architecture in LLMs?

The transformer is the breakthrough architecture from 2017 that made modern LLMs possible. Its key idea is 'attention,' allowing every token to look at every other token to capture long-range relationships in parallel, which is crucial for processing complex text.

What is the 'context window' in large language models?

The context window defines how many tokens an LLM can hold in mind simultaneously, ranging from a few thousand to over 1 million. This window contains the user's prompt, any pasted documents, and the model's own reply so far, influencing how much information it can process before answering.

Why do large language models sometimes hallucinate?

LLMs hallucinate because they are trained to produce plausible text, not verified truth, meaning they can confidently state something false as fact. There is no built-in fact-checker; users should treat their output as a fast first draft to verify, not as an oracle.