Recent advancements in Large Language Models (LLMs) have led to the development of sophisticated AI systems that can generate human-like text. GPT-3, for instance, is a popular LLM trained on vast amounts of data from various sources, including books, articles, and websites like Wikipedia, Reddit, and Stack Overflow.

The training process involves pre-processing the input data by removing explicit or biased content, labeling text, parsing text, and converting it into numerical forms called word embeddings. The model starts with an empty brain, similar to a newborn child, and is then shown the numerical version of the data. Through training, the model adjusts its parameters to improve performance.

When generating human-like responses, LLMs like GPT-3 split input sentences into words or tokens and convert them into numerical forms. The model calculates attention scores using these vectors to determine which words are most important in the input prompt. By searching its trained data, the model can generate output word by word using next-word probability.

This innovative approach enables LLMs to produce human-like text that meets user needs. As AI technology continues to evolve, we can expect even more impressive language generation capabilities.

Source: https://dev.to/iihsan/how-do-llms-like-gpt-generate-human-like-text-8n9