We have very little understanding of how "thoughts" work when it comes to humans. By comparison we know exactly what these large language models are doing, and it literally _is_ just completing word sequences. That is precisely what they are tasked to do. Training these models is primarily done in one of two ways (or both):
- feeding it a ton of text, masking certain words or portions of the text, and then defining a simple objective function of correctly filling in the masked portions
- feeding it a ton of text, and defining a simple objective function of correctly generating the next [few/many/N] tokens
This is also precisely why there has been so much discussion around whether these models are even learning language or if they are simply memorizing all the possible patterns.
When you ask a human, "what did you eat for lunch?", we make a series of choices and recall bits of information to answer that. If the brain truly does operate the same way as a neural network, then at simplest levels we use a highly efficient multimodal model. That is very, very different from a language model that needs to essentially read more of the internet than is possible for any human to do in their lifetime, and even then only come somewhat close to human levels of text generation.