Large language models (LLMs) are used for a wide range of natural language processing (NLP) tasks, including :

1. Text generation : LLMs can generate new text that is similar to human writing, making them useful for applications like chatbots, content creation, and writing assistance.

2. Text completion : LLMs can predict and generate the next word or sentence in a piece of text, making them useful for applications like autocomplete, spell-checking, and writing assistance.

3. Text classification : LLMs can classify text into different categories based on its content, making them useful for applications like sentiment analysis, topic modeling, and spam filtering.

4. Translation : LLMs can translate text from one language to another, making them useful for applications like language localization, multilingual search, and cross-language communication. 5. Question answering : LLMs can answer questions based on their understanding of text, making them useful for applications like virtual assistants, customer service bots, and educational platforms.

6. Summarization : LLMs can summarize long pieces of text into shorter summaries, making them useful for applications like news aggregation, document summarization, and content curation.

7. Dialog systems : LLMs can generate natural language responses in conversation with humans, making them useful for applications like chatbots, customer service agents, and personal assistants.

Large language models are useful in any application that requires understanding or generation of human language. Their flexibility and versatility make them a powerful tool in the field of natural language processing. One of the most widely used LLM-based AI chatbots is ChatGPT, which is based on OpenAI's GPT-3 model.