An Introduction to Large Language Models

Large language models are indispensable in the world of Al. Their applications vary from chatbots and sentiment analysis to content generation and data analysis. While they have evolved a lot in recent years, some challenges remain.

- Phani Kiran

Languages have some fundamental characteristics. They are mostly contextual, and have both explicit and implicit meanings. Syntax and semantics are at their core. Also, they are usually understood along with their cultural underpinnings.

Large language models (LLM) are basically neural networks that are designed to process large amounts of natural language data. These models are trained with vast amounts of data; they help to generate responses to user queries by understanding the context and intent of the user’s question and providing responses. They use advanced deep learning algorithms to accomplish this.

Image recognition uses convolutional neural networks whereas language models are based on recurrent neural networks. The feedback is used for training the models. In fact, LLMs use a combination of various neural networks to provide the most approximate output.

Factors that led to the growth of LLMs