Large Language Models — How LLMs Actually Work, Explained

Overview

Large language models (LLMs) are the AI behind chatbots and coding assistants, generating human-like text by predicting the next token. They are trained on trillions of tokens using a transformer architecture, which involves pre-training for knowledge and fine-tuning for helpfulness. Understanding how LLMs work, their context window, and the risk of hallucination is crucial for effective interaction.

In this video

• Large language models fundamentally predict the next token, which is a chunk of text roughly four characters long.
• Modern models carry hundreds of billions of adjustable weights, enabling skills like translation, summarizing, reasoning, and code writing.
• Training involves two stages: pre-training for knowledge acquisition by predicting tokens, and fine-tuning with human feedback to teach helpfulness and instruction following.
• The transformer architecture, introduced in 2017, uses 'attention' to allow every token to consider all other tokens for what comes next, facilitating long-range relationship capture in parallel.
• Large language models operate with 'tokens' and have a 'context window' that determines how many tokens (from a few thousand to over a million) they can hold in mind at once.
• Replies are generated one token at a time, with a 'temperature' setting controlling the adventurousness or predictability of the chosen next token.
• Hallucination occurs because models are trained to produce plausible text, not verified truth, and do not possess a built-in fact-checker.
• To address hallucination, users should ground the model with real sources, connect it to search or tools, and verify any critical output.
• Alignment is an ongoing discipline using human feedback and guardrails to shape a model to be helpful, honest, and harmless, despite persistent challenges like bias and jailbreaking.
• A large language model's output should be treated as a fast first draft to verify, never as an oracle.

Frequently asked questions

What is a large language model?

Large language models (LLMs) are AI systems that generate fluent, human-like text on demand, serving as the technology behind chatbots and coding assistants. Their core function is to predict the next token in a sequence.

How are large language models trained?

Training occurs in two stages: pre-training, where the model reads vast amounts of text to predict the next token and gain knowledge, followed by fine-tuning, which includes reinforcement learning from human feedback to teach it to be helpful and follow instructions.

What is the transformer architecture?

The transformer is a breakthrough architecture from 2017 that made modern LLMs possible. Its key idea is 'attention,' allowing every token to look at every other token to decide what matters for the next word, capturing long-range relationships in parallel.

Why do large language models hallucinate?

LLMs hallucinate because they are trained to produce plausible text rather than verified truth and lack an internal fact-checker. This can lead them to confidently state false information.

How can I prevent a large language model from hallucinating?

To prevent hallucination, you should 'ground' the model by giving it real sources to quote, connecting it to search or tools, and always verifying any information that matters. Treat its output as a draft, not an oracle.