Large Language Model (LLM)
AI models with billions of parameters trained on vast text datasets to understand and generate human language.
Detailed Definition
A Large Language Model (LLM) is a sophisticated AI system with billions or even trillions of parameters, trained on enormous datasets of text to understand and generate human language with remarkable fluency and capability. LLMs represent a significant breakthrough in natural language processing, demonstrating emergent abilities that weren't explicitly programmed but arise from the scale and complexity of their training. These models can perform a wide variety of language-related tasks including text generation, translation, summarization, question answering, coding, and creative writing. Examples include GPT-4, Claude, PaLM, and LLaMA. LLMs are typically based on transformer architecture and trained using self-supervised learning on diverse text sources. Their large scale enables them to capture nuanced patterns in language and demonstrate sophisticated reasoning capabilities. LLMs have become the foundation for many AI applications and represent a major step toward more general artificial intelligence.
Core TechnologiesMore in this Category
Autoregressive Model
A type of model that predicts the next element in a sequence based on previous elements.
BERT
Bidirectional Encoder Representations from Transformers - a pre-trained language model.
Deep Learning
A subset of machine learning using neural networks with multiple layers to learn complex patterns.
Embedding
A numerical representation of data that captures semantic meaning in a high-dimensional vector space.
GPT (Generative Pre-trained Transformer)
A family of language models that generate human-like text using transformer architecture.
Neural Network
A computing system inspired by biological neural networks that learns to perform tasks by analyzing examples.