What are large language models and how do they work

· Category: AI & Machine Learning

Short answer

Large language models (LLMs) are neural networks trained on massive text datasets to predict the next token in a sequence. They use the transformer architecture with self-attention mechanisms that capture relationships between all words in the input. For the basics of neural networks, see how to build a neural network from scratch.

How transformers work

  1. Tokenization: Text is split into tokens (words or subwords)
  2. Embedding: Tokens are converted to dense vector representations
  3. Self-attention: Each token attends to all other tokens, computing relevance scores
  4. Feed-forward layers: Process the attention output through neural network layers
  5. Output: Predicts probability distribution over the vocabulary for the next token

Key concepts

  • Parameters: The weights learned during training. GPT-4 has over 1 trillion parameters.
  • Context window: The maximum number of tokens the model can process at once
  • Fine-tuning: Additional training on domain-specific data to improve performance
  • RLHF: Reinforcement Learning from Human Feedback aligns models with human preferences

Tips

  • LLMs can hallucinate — always verify factual claims
  • Use prompt engineering to guide outputs effectively