ChatGPT is sexy! But how the heck does it (large language models) work

Large language models are a type of artificial intelligence that uses deep learning algorithms to analyse vast amounts of text data to understand natural language and generate human-like responses. These models can perform a variety of tasks, such as language translation, chatbots, content summarisation, and even creative writing.

There are different architectures of large language models, such as GPT (Generative Pre-trained Transformer), BERT (Bidirectional Encoder Representations from Transformers), and T5 (Text-to-Text Transfer Transformer). These models work by breaking down sentences into smaller components called tokens and then mapping them to a high-dimensional vector space where their meaning is represented. The models can then perform various operations on these vector representations, such as predicting the next word in a sentence or generating a response to a prompt.

One of the most popular examples of large language models is GPT-3, which has 175 billion parameters and is currently the largest language model in production. It can perform a wide range of natural language processing tasks, such as translation, summarisation, and question answering, and can even generate coherent and creative text.

To develop a large language model, a team of researchers typically starts by selecting a dataset that is large and diverse enough to capture the nuances and variations of natural language. The team then trains the model on this dataset using deep learning techniques such as back propagation, which involves adjusting the model's parameters to minimize the error between the predicted output and the actual output.

The training process can take weeks or even months, depending on the size of the dataset and the complexity of the model. As the model learns from the data, it becomes better at predicting natural language, and its performance improves.

To keep improving the model, researchers can fine-tune it on specific tasks or domains by exposing it to additional training data that is relevant to the task. They can also use unsupervised learning techniques, such as self-supervised learning, to train the model on unlabelled data and improve its understanding of natural language.

Let's say a user inputs the following question: "What is the capital of France?"

When this question is received by ChatGPT, it first tokenizes the input by breaking it down into smaller units called tokens. These tokens are essentially words or subwords that are used to represent the meaning of the input text.

Next, ChatGPT uses its deep learning algorithms to generate a response to the input question. It does this by analyzing the tokens in the input question and using its pre-trained knowledge of natural language to generate a coherent response.

In this case, ChatGPT might generate a response like: "The capital of France is Paris."

This response is generated using the contextual information learned from the massive amounts of text data that ChatGPT has been trained on, along with the specific information contained in the input question. In this case, ChatGPT recognized the input question as a request for information about the capital of France, and it used its pre-existing knowledge of geography and country capitals to generate the response.

Overall, response generation in ChatGPT is based on its ability to understand and analyze the meaning of natural language inputs, and then generate relevant and coherent responses based on that understanding. This process is made possible by the complex deep learning algorithms and massive amounts of training data that are used to develop and improve the language model.

In summary, large language models work by analyzing vast amounts of text data using deep learning algorithms to generate human-like responses. These models are developed by training them on large and diverse datasets using techniques such as back propagation, and they are continuously improved by fine-tuning on specific tasks or domains and using unsupervised learning techniques to train on unlabelled data.