Large Language Models (LLMs) are advanced neural networks with billions of parameters, trained on extensive text data using semi-supervised learning. They excel at tasks such as mathematical reasoning and sentiment analysis, allowing them to understand the structure and meaning of human language. LLMs can analyze unstructured data like customer reviews and social media interactions, providing valuable insights for businesses.

Unlike traditional machine learning models that work best with structured data, LLMs are designed to handle unstructured data, such as text files. They require minimal data preprocessing and can automatically learn patterns from raw text. However, LLMs face challenges with interpretability and may produce outputs that are not always accurate.

Businesses are leveraging LLMs for various applications, including sentiment analysis, Named Entity Recognition (NER), and text generation. These capabilities enable companies to analyze customer feedback, monitor social media trends, and automate tasks through chatbots. For instance, platforms like Sprinklr use LLMs to track brand sentiment on social media, helping businesses understand customer behavior and preferences.