Comment by LoganDark

For autoregressive models (practically all hosted ones), it's because of the nature of next-token prediction. LLMs lock themselves into a particular sentence structure ahead of time and have to guess at the rest of the sentence. Samplers have no insight into the LLM's "intent" aside from the probability of each next token, and the LLM has no insight into its previous "intent" that resulted in a given probability in the first place. I don't know if this is possible to solve with more training, I think a fundamental architectural shift may be needed, like more research into diffusion language models.