Large Language Models

A Simple Explanation - By Varsha Saini

Large Language Models or LLMs are language models that are trained using deep learning models on vast amounts of text data in order to learn patterns and relationships between words, phrases, and sentences.

LLMs can be used to perform a wide range of natural language processing tasks, such as text classification, translation, summarization and question answering. The larger the model, the more data it has been trained on, and the more accurate its predictions can be.

Large Language Models Example

  • BERT
  • GPT-3
  • XLNet
  • T5
  • ChatGPT

What are Large Language Models Used For?

Large language models (LLMs) have a wide range of uses in natural language processing (NLP). Some of the interesting applications of LLMs include:

  1. Code Generation
  2. Website Creation
  3. SQL Query Generation
  4. Chatbots
  5. Code Debugging
  6. Product Insights

Overall, LLMs have the potential to revolutionize the field of natural language processing, and their applications are only limited by the creativity of researchers and developers.

Bloom is the world's largest open-source multilingual language model. It is able to generate text in 46 natural languages and 13 programming languages.