Large Language Models (LLMs) are a type of machine learning model that can perform a variety of natural language processing (NLP) tasks, including text generation and classification, answering questions in a conversational manner, and translating text from one language to another.
The term "large" refers to the number of values (parameters) that the model can autonomously change during learning. Some of the most powerful LLMs have hundreds of billions of parameters.
LLMs are trained with enormous amounts of data and use self-supervised learning to predict the next token in a sentence given the surrounding context. This process is repeated again and again until the model achieves an acceptable level of accuracy.
Large Language Models are used for low or zero-shot scenarios, when little or no domain-specific data is available to train the model.
Low or zero-shot approaches require that the AI model has good inductive bias and the ability to learn useful representations from limited (or non-existent) data.
In conclusion, Large Language Models (LLMs) have revolutionized the field of natural language processing (NLP) by enabling machines to perform a wide range of tasks, including text generation, classification, and translation. The massive amount of data and self-supervised learning techniques used to train LLMs have led to breakthroughs in language understanding and generation. As we continue to develop and fine-tune LLMs, we can expect to see even more impressive capabilities and applications in the future.