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Transformer-based LLMs generate text by predicting the next token in a sequence based on all previous tokens using attention mechanisms. This is the core principle of autoregressive language modeling where the model processes the entire context of previous tokens to generate the most probable next token. The attention mechanism allows the model to weigh the importance of different parts of the input sequence when making predictions, enabling it to capture long-range dependencies and contextual relationships in text.