A neural network trained to predict and generate sequences of tokens.
A large language model learns statistical relationships among tokens from a large training corpus. A transformer uses attention to combine information from the current context, then predicts a probability distribution for the next token. Post-training can make that base model more useful at following instructions, using tools, and refusing unsafe requests, but it remains a probabilistic model rather than a database or a person.