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code_biologist a day ago

Writing and videos produced before 2022 are the information equivalent of low-background steel (steel produced before the atom bomb era). Not all are precious, but they are important. AI influences are so pervasive at this point, even in informational writing from domain experts.

Relentless grounding is my personal solution for my AI epistemic crisis, but it's an expensive solution in terms of time and effort, and triage is hard too.

nomel a day ago | parent [-]

What is "grounding"?

code_biologist 8 hours ago | parent | next [-]

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.

papercrane a day ago | parent | prev | next [-]

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.

ColdStream a day ago | parent | next [-]

That works ok until the citation material ends up with LLM output being fed in. Already there are many examples of this happening.

papercrane a day ago | parent [-]

Yes, the trick is having a "ground-truth" KB it can link to, and trying to avoid citogensis from happening. I'm not sure if it's a technique that's every going to scale because of that.

stingraycharles a day ago | parent | prev [-]

I don’t think this is correct, as we’re talking about all online discourse being influenced by AI, and you don’t know what to trust anymore, rather than generating AI output yourself (which is what you are referring to).

papercrane a day ago | parent [-]

In this context, it would mean building a knowledge base the model can link to that is considered authoritative. So maybe the LLM is has some slop in it's training, and that causes it to try and output some junk, but if it can't match it to it's verified ground-truth KB it doesn't end up outputting it.

The problem is figuring out what is the authoritative sources.

stingraycharles a day ago | parent [-]

But we're talking about AI writing and/or influencing other people's online discourse, not?

stingraycharles a day ago | parent | prev | next [-]

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

a day ago | parent | prev [-]
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