What is "grounding"?

I do mean "grounding" as seeking the primary source of information by hand as my sibling commenter said.

If an LLM says something about history, have it give you source documents so that you can verify. If it says Earth's gravitational constant is 98 [sic] m/s^2, have it generate a prediction of a timed 1m paperclip drop test, and go do it yourself.

It's perhaps excessive in the case of gravity, but for things that matter like medical stuff I tell LLMs to expose their step by step causal reasoning / inference, and then check whether I buy it myself. SotA models' biochemistry + neurochemistry is often pretty solid. Often their answers are based on a slightly hallucinated understanding if it's a spatial anatomy question. It's fun when you get some research paper citations and then upon reading them decide you don't agree with the underlying research (eg. fMRI studies often make broad / interesting claims based on small sample sizes or sample populations that are clearly biased).

To be clear, I don't think that the LLM's self explained reasoning in a single turn is how it actually arrived at a given conclusion, but I just want to expose and bind the final conclusion to a verifiable epistemic chain.

It's when the models outputs are connected to sources.

So for example, you might have a company chatbot that responds with a summary of a Confluence page it was trained on and a link to the source.

Doesn't eliminate hallucinations, but it does reduce them and gives you a chance to verify.

I think in this context, they mean seeking the primary source of the information by hand.