this post was submitted on 11 Mar 2026
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Okay, this is looking promising, at least in terms of the most important qualifications being plainly stated in the opening line.
Because the amount of hallucinations/inaccuracies “in the wild” - depending on the model being tested - runs about 60-80%. But then again, this would be average use on generalized data sets, not questions focusing on specific documentation. So of course the “in the wild” questions will see a higher rate.
This also helps users, as it shows that hallucinations/inaccuracies can be reduced by as much as ⅔ by simply limiting LLMs to specific documentation that the user is certain contains the desired information, rather than letting them trawl world+dog.
Very interesting!
I have been saying this for awhile. I am sorta hoping we see open source llms that are trained on a curated list of literature. its funny that these came out and it seemed like the makers did not take the long known garbage in - garbage out into account.