What is Large Language Model?
Also known as: LLM
A Large Language Model (LLM) is an AI system trained on massive amounts of text to understand and generate human-like language. It powers tools that can answer questions, write content, translate, and hold conversations.
LLMs are built on transformer neural networks that process text as sequences of tokens. They learn statistical patterns by predicting the next token during training on enormous datasets.
The 'large' part refers to models with billions of parameters that capture complex language structures, context, and world knowledge from sources like books and websites.
After training, LLMs generate responses by sampling likely next tokens based on a user's prompt, enabling flexible language tasks without task-specific programming.
Example
When you ask ChatGPT to 'explain photosynthesis to a 10-year-old,' the underlying LLM generates a simple, accurate explanation by drawing on patterns learned from vast educational text during training.
Why it matters
LLMs have made advanced natural language capabilities widely accessible, transforming search, coding assistants, education, and creative work while raising new questions about accuracy and ethics in AI.
Frequently asked questions
It refers to the enormous number of parameters (often billions) and the huge scale of training data required to achieve strong language performance.
Related terms
A Transformer is a neural network architecture that processes sequential data like text using self-attention to weigh relationships between all parts of the input at once.
A neural network, or artificial neural network (ANN), is a computational model inspired by the human brain that learns to recognize patterns in data by passing information through layers of interconnected artificial neurons.
Generative AI (GenAI) is artificial intelligence that learns patterns from data to create new, original content such as text, images, audio, or code.
Natural Language Processing (NLP) is a branch of artificial intelligence that enables computers to understand, interpret, and generate human language in useful ways.
Fine-tuning is the process of taking a pre-trained AI model and continuing its training on a smaller, task-specific dataset to adapt it for a particular use case.
The attention mechanism is a technique in neural networks that lets the model dynamically focus on the most relevant parts of the input when processing each element, rather than treating all inputs equally.