![[instruct-gpt-architecture.png]] [Intruct GPT paper](https://arxiv.org/abs/2203.02155) A large language model is an artificial intelligence system that has been trained on a vast amount of natural language data, using deep learning algorithms. These models are capable of generating human-like language output, such as sentences, paragraphs, and even entire articles or stories. Large language models typically use neural networks to analyze and understand the patterns and structures of language. They are trained on massive datasets of text, such as books, news articles, and web pages, which allows them to learn the nuances of language and make predictions about what words or phrases are likely to come next in a given context. Some of the most advanced large language models, such as GPT-3 and BERT, have billions of parameters and can generate extremely convincing language output (GPT-3 model has 175 billion parameters). These models can generate human-like text by predicting the next word in a sentence based on the context provided by the previous words. In 2022 OpenAI introduced Instruct GPT which allows users to fine-tune the language generation capabilities of the GPT (Generative Pre-trained Transformer) model. This tool has been designed to make it easy for developers and researchers to fine-tune GPT for a wide range of natural language processing (NLP) tasks, such as language translation, text summarization, and question answering. Open AI used reinforcement learning with human feedback (RLHF) to fine-tune the original GPT-3 model using a large dataset of instructions and tasks, to allow it to learn to understand the meaning of instructions and how to complete the tasks. The fine-tuning process is known as transfer learning, where the model learns to adapt to the new task by leveraging the knowledge it has already acquired during pre-training. By leveraging its pre-training on a large corpus of text data, Instruct GPT can generalize its knowledge to new classes with very few examples, making it a good candidate for the use of few-shot learning. This enables the model to learn and make predictions based on very limited amounts of data.