LLMs generate text by predicting the next token in a sequence based on context provided in the input. First, the input text is tokenized into smaller units (tokens) and converted into numerical embeddings. These embeddings pass through multiple transformer layers, where attention mechanisms weigh the importance of each token in relation to the context.
The model outputs probabilities for the next token, and the most likely token is added to the sequence. This process repeats iteratively until the desired output length is reached or a stop condition, like an end-of-sequence token, is met. For example, given the prompt “Write a story about a robot,” the LLM generates a coherent story one token at a time.
Parameters like temperature and top-k sampling influence the variability and creativity of the generated text. Lower temperatures produce deterministic outputs, while higher values allow more diverse and creative responses. This mechanism enables LLMs to create outputs tailored to various applications, from factual summaries to imaginative storytelling.