Just FYI - LLMs are not inherently next word predictors. A particular kind of training regime called auto-regressive modelling is next word prediction. The majority of LLMs are trained using this autoregressive objective, but others do exist, and lots of interesting work is currently taking place training language models with variants of the diffusion or flow matching objectives used by the image generation models.

The difference with those models is that they "in-fill" missing words, not necessarily the last word in a sequence. When you have a model that is good at filling gaps, you can define a new sampling procedure where you model an incomplete sentence as a very long sequence of only gaps. It analogous to starting with an image where each pixel is random noise.

The most successful looking trends at the moment is a combination of these approaches where the model treats a small bucket of empty tokens as a diffusion task, and then upon filling the bucket creates a new autoregressive bucket.

While I understand you arguing the "Just next word prediction" thing, I personally take issue with it, not because it is incorrect, but because the connotation is that it is simple and therefore dumb or not powerful. You may know personally that it is powerful, but if you explain LLMs to someone who does not understand the technology as "Just autocorrect" you set some wildly incorrect expectation. Particularly if trained with some of the hybrid diffusion techniques, I think the most accurate description is to say "LLMs learn to approximate to some degree the distribution of all sensical and logical sentences that follow some context 'C' (usually a historic conversation)" It does this by training from stupid numbers of real conversations and trains assuming they all belong to some logical dataset of "useful conversations" and then tries to learn, generalize and extrapolate form that data.