LLMs are models of language, and language is a model of the world. So we have a model of a model of the world, but a LLM does not get much grounding in the real

LLMs are models of language, and language is a model of the world. So we have a model of a model of the world, but a LLM does not get much grounding in the real world. There is a phase of training called multi-task instruction tuning where the LLMs solve problems and thus are grounded in exact answers. That makes the difference between the difficult to handle GPT-3 from 2020 and the better behaved GPT-3 of 2022. But that dataset is small by comparison to the raw text used in pre-training, it won't do the grounding perfectly. Real grounding comes from real feedback, even humans need the feedback or we are just going on wild tangents.