LLMs are language models, something being a transformer or next-state predictor does not make it a language model. You can also have e.g. convolutional language models or LSTM-based language models. This is a basic point that anyone with any proper understanding of these models would know.

Even if you disagree with these semantics, the major LLMs today are primarily trained on natural language. But, yes, as I said in another comment on this thread, it isn't that simple, because LLMs today are trained on tokens from tokenizers, and these tokenizers are trained on text that includes e.g. natural language, mathematical symbolism, and code.

Yes, humans have incredibly limited access to the real world. But they experience and model this world with far more tools and machinery than language. Sometimes, in certain cases, they attempt to messily translate this messy, multimodal understanding into tokens, and then make those tokens available on the internet.

An LLM (in the sense everyone means it, which, again, is largely a natural language model, but certainly just a tokenized text model) has access only to these messy tokens, so, yes, far less capacity than humanity collectively. And though the LLM can integrate knowledge from a massive amount of tokens from a huge amount of humans, even a single human has more different kinds of sensory information and modality-specific knowledge than the LLM. So humans DO have more privileged access to the real world than LLMs (even though we can barely access a slice of reality at all).