A large language model (LLM) is a type of artificial intelligence system that is trained on vast amounts of text data to understand and generate human-like language.
LLMs are based on transformer neural network architectures and can perform a wide range of natural language processing tasks such as text generation, summarization, translation, and question answering.
LLMs acquire their language abilities by learning statistical patterns from massive text corpora during training, allowing them to understand syntax, semantics, and context.
Notable examples include OpenAI's GPT series (GPT-3, GPT-4), Google's PaLM, Meta's LLaMA, Anthropic's Claude, and IBM's Granite models.
LLMs can be fine-tuned or prompted for specific tasks, enabling applications like conversational AI assistants, content generation, language translation, and code writing.
While highly capable, LLMs can inherit biases and inaccuracies from their training data, requiring techniques like prompt engineering and reinforcement learning to mitigate these issues.
LLMs are a type of foundation model, trained on broad data to provide general capabilities, in contrast to narrow task-specific models.
LLMs have enabled significant advances in natural language processing and generative AI, but also raise concerns around security, privacy, and the potential for misuse.
In summary, large language models are a transformative AI technology that can understand and generate human-like text, enabling a wide range of language-based applications while also presenting challenges around responsible development and deployment.