Advanced neural network models trained on vast text corpora to understand and generate human language. LLMs use self-attention mechanisms to process and generate text based on patterns and relationships learned from their training data.

LLMs represent a significant advancement in natural language processing, capable of understanding context, nuance, and implied meaning in text. They can perform various language tasks, including translation, summarisation, question answering, and creative writing. Modern LLMs like GPT-4, Claude, and LLaMA contain billions of parameters, allowing them to capture complex linguistic patterns and world knowledge. Despite their capabilities, LLMs have limitations, including potential for generating factually incorrect information (hallucinations), biases inherited from training data, and difficulties with certain types of reasoning.

‍

An enterprise customer service department using an LLM to draft responses to customer inquiries, generate documentation, or summarise customer feedback trends, with human review before final deployment.