Large language models are a type of artificial intelligence model designed to understand, generate, and manipulate human language. They are based on neural networks, specifically deep learning architectures, which are trained on massive amounts of text data to learn patterns, grammar, and semantic meanings in language.

One of the most popular and advanced architectures for large language models is the Transformer, which was introduced by Vaswani et al. in 2017. Since then, several iterations and improvements have been made, leading to models like GPT (Generative Pre-trained Transformer) by OpenAI, which has advanced to GPT-3 and beyond.

Large language models can perform various natural language processing (NLP) tasks, such as machine translation, text summarization, question-answering, sentiment analysis, and more. They can generate coherent and contextually relevant text, making them useful for applications like chatbots, content generation, and information retrieval.

However, these models also have limitations and challenges, including biases present in the training data, potential for misuse, and substantial computational resources required for training and deployment. Researchers and developers continue to work on refining and addressing these challenges to make large language models even more effective and responsible tools.
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence