Autoregressive language-based models learn the probability of the next token in a sequence conditional upon previously observed tokens. Therefore, de novo string generation can be conducted by supplying a start token (e.g., "GO") and predicting the probability over the next token in the vocabulary, sampling from that probability distribution to select the next token (e.g., "C"), and then using the sampled token as the input in the subsequent iteration. This is repeated until a stop token (e.g., "EOS") is sampled indicating the termination of generation. However, an initial sequence of tokens can be provided from a user-specified prompt upon which further generation is conditional, as is the case with large-language models such as GPT .