Large language model

Large language models (LLMs) are a type of machine learning model designed for natural language processing (NLP) to understand, generate, and interact with human language. They are trained on massive datasets—like books, articles, and social media posts—to learn patterns in language, context, and meaning.

LLMs can generate coherent, contextually appropriate text, ranging from articles and emails to poetry and code. Some models are multimodal, meaning they can process not only text but also images, audio, and other data types. They can track conversation history, answer questions, analyze sentiments, summarize content, and translate languages.

LLMs are also widely used in coding, where they can write, debug, document, and explain code. Additionally, they power conversational AI such as chatbots, virtual assistants, and customer support systems.

LLMs are built on transformer-based neural networks. Input text is first split into tokens and converted into numerical vectors called embeddings. These embeddings are then processed through layers of self-attention and feedforward networks, which help the model understand relationships between words and predict the most likely next token in a sequence.

Some well-known LLMs include OpenAI’s GPT-5, Anthropic’s Claude 4, Google DeepMind’s Gemini 2.5, and DeepSeek‑R1.