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I have not yet read the paper, but based on this description it seems like it provides grounding in the context of the training data, which is kind of the rub with current LLMs to begin with, right? We don't have a set of high quality training data that is completely unbiased and factual.

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> … which is kind of the rub with current LLMs to begin with, right?

No, the bigger problem with current LLMs is that even with high quality factual training data, they often generate seemingly plausible nonsense (e.g. cite nonexistent websites/papers as their sources.)

This is by design imo; they’re trained to generate ‘likely’ text, and they do that extremely well. There’s no guarantee for faithful retrieval from a corpus.

Important addition to your partially right statement: "they’re trained to generate ‘likely’ text" is they are trained to produce most probable next word so that the current context look as "similar" to training data as possible. Where "similar" is not "equal".

I'd describe it as grounding the model with a formally specified symbolic world model.

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