Large Language Models (LLMs)

Large Language Models (LLMs) refer to fundamental machine learning models that utilize deep learning algorithms for understanding and analyzing natural language. LLMs are capable of generating human-like text by predicting the probability distribution of the next word or sequence of words given the context of previous words. These models are typically trained on massive amounts of text data using deep learning techniques, such as recurrent neural networks (RNNs) or transformers.

Large Language Models (LLMs) are unique in their ability to carry out a diverse set of language-related functions, including language translation, sentiment analysis, chatbot dialogues, and more. They have the capacity to comprehend intricate textual data, recognize entities, infer relationships between them, and produce human-like responses to dialogue or other natural language inputs which are coherent and grammatically correct. LLMs are widely popular due to their potential to perform various natural language processing activities like text summarization, language translation, question answering, and text completion. One of the most well-known LLMs is OpenAI's GPT (Generative Pre-trained Transformer) series.