Large Language Models (LLMs): Hypes and Realities
The basic principles and features of large language models, a type of AI model that is trained on vast amounts of text data to understand and generate human-like language outputs, are studied.
Abstract
Artificial intelligence (AI) has created a lot of buzz in recent years. Using machine learning and other AI techniques several intelligent initiatives have been tested. The large language model is one of them. A large language model (LLM) normally refers to a type of AI model that is trained on vast amounts of text data to understand and generate human-like language outputs. These models are designed to capture the statistical patterns and structures present in the training data, enabling them to generate coherent and contextually relevant responses. The widely known ChatGPT is one of the LLMs which can do several tasks and answer many questions. It is trained with a huge number of data sets and a large number of parameters. In addition to ChatGPT, many other LLMs such as the Google Bard, Claude v1, Bison 001, Cohere, Falcon, and Guanaco-65B have surfaced in recent times. In this paper, we study the basic principles and features of LLMs. We go through their brief history, abilities, limitations, challenges and future prospects.