A large language model (LLM) is a type of artificial intelligence (AI) program designed to understand, generate, and work with human language text. LLMs are built using deep learning techniques, specifically transformer neural networks, which enable them to analyze vast amounts of text data and learn the relationships between words, sentences, and concepts
. Key characteristics of LLMs include:
- Training on massive datasets: LLMs are trained on enormous collections of text, often sourced from the internet, books, and other large corpora, allowing them to capture complex language patterns and nuances
- Transformer architecture: This neural network design uses mechanisms like self-attention and positional encoding to process entire sequences of text in parallel, making LLMs efficient and powerful at understanding context
- Billions of parameters: LLMs have a large number of adjustable variables (parameters) that help them model language intricacies and generate coherent, contextually relevant responses
- Versatility: They can perform a wide range of language-related tasks such as text generation, summarization, translation, question answering, and even creative writing or code generation
LLMs are often referred to as foundation models because they provide a base for many AI applications and can be fine-tuned for specific tasks. Examples include OpenAI's GPT series, Google's BERT and PaLM, and Meta's LLaMA models
. In summary, an LLM is a sophisticated AI system that leverages deep learning and large-scale data to understand and generate human language, enabling numerous applications across industries and technologies
