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In “We’re not taking the fact-checking powers of AI seriously enough. It’s past time to start.”, digital literacy expert Mike Caulfield researches how Grokipedia appears to have correctly fact-checked a claim that professional fact-checkers missed.

In the article, Caulfield demonstrates his 3000+ word “Deep Background” instruction prompt, a calibrated directive to guide an LLM through a detailed fact-checking exercise. He uses this prompt for a successful micro-investigation into a claim relating to the Nobel Prize ceremony and makes the case for an augmented approach to fact-checking.

But I remain convinced that there is no future of verification and contextualization that doesn’t involve both better understanding of LLMs and more efficacious use of them.

Caulfield also challenges “hallucination” as a flawed catch-all for the ways in which LLMs are inaccurate. Modern LLMs are more likely to be conflating through extrapolation or overweighting unreliable sources rather than purely fabricating.

His hotly contested stance is that disengagement is a mistake. One must engage with the technology in order to understand it.

My set of understandings led me to discover a substantial error by a news organization that, if search hasn’t failed me, seems to have been missed by everyone up until now. ⁠⁠What has “I can’t analyze the output because its [sic] meaningless fancy autocomplete” done for you?

I applaud Caulfield for raising awareness of the successful AI-assisted fact-check by Grokipedia despite feeling negatively about the site itself.

I was initially buoyed by Caulfield’s argument, but on reflection I think he still gives Grokipedia too much credit. I think we’ll need more than a single example of a fact-check slip made by humans and corrected by AI to make a case that this is revolutionary technology for fact-checkers.