I genuinely don't know what you mean by "blind probability" or "surface-level probability", but in case you don't know, how transformer models work is after tokenization and embeddings are calculated each attention head in the model calculates "self-attention" which is the relationship between all tokens in the current context of the model and passes its results either to another layer of attention heads or to the decoder layer of the model. This typically uses a temperature parameter to control randomness in the output by affecting the probability distribution of tokens in the vocabulary when producing output. I'm not sure whether this counts as "blind" or "surface-level" but that's the probability in GPT models. If you want to learn more, here's Vaswani et al 2017 - the seminal paper https://arxiv.org/pdf/1706.03762.pdf