Large language models (LLMs) work by using deep learning, a type of machine learning, to understand and generate human language. Here's a concise explanation of how they work:
- LLMs are neural networks, inspired by the human brain, consisting of layers of interconnected units called neurons. They use a specific architecture called transformers that help understand relationships between different parts of text, no matter how far apart in the sequence.
- These models are trained on vast amounts of text data—trillions of words from books, articles, websites, and code repositories—to learn patterns and context. Training is typically unsupervised, meaning the model learns by recognizing statistical relationships between words without explicit instructions.
- Text is broken down into smaller units called tokens (words, subwords, or characters), which are converted into numeric representations called embeddings. These embeddings capture the meanings and context of the tokens.
- During training, the model learns to predict the next word or token in a sentence based on the preceding words, using probability. This process helps it generate coherent and contextually relevant text.
- After initial training, LLMs can be fine-tuned or prompt-tuned for specific tasks, such as translation, answering questions, or writing code. Fine-tuning adjusts the model with additional labeled data, while prompt-tuning guides the model using examples in the input.
- Key mechanisms include attention, which lets the model focus on important parts of the input text, and multiple neural network layers that process and transform data to understand complex language structure.
- When you interact with an LLM, it takes your input, encodes it, and decodes it to produce a predicted output, such as a sentence continuation or an answer.
In summary, LLMs work by learning from massive text data through neural network architectures to predict and generate natural language based on patterns and context learned during training.
