What is a large language model (LLM) primarily used for?

A large language model (LLM) is primarily utilized for creating models that analyze patterns in text-based tasks. LLMs excel at understanding and generating human language, making them well-suited for various applications, including text completion, summarization, translation, and conversation generation. By leveraging vast amounts of textual data during training, these models can discern intricate patterns, context, and relationships within language, enabling them to perform tasks that involve processing and generating text.

The primary focus on text-based tasks sets LLMs apart from other types of models that may be designed for different purposes, such as statistical analysis, interpreting complex data structures, or generating numerical predictions. While those tasks are important in their own right, they typically rely on different types of algorithms and methodologies that do not emphasize the nuances of language as LLMs do.