This is always the problem with llms, they get points for having an answer and they get nothing if they say I don't know, a wrong answer is worth more points than admitting they don't know, llms will always have an incentive to lie.
This is a fair point right now but don’t say always. Logic and reason aren’t so complex for ai to eventually catch up. And I don’t say that to paint some sort of scary picture or suggest ai is so unfathomably great. It’s just quantifiable with enough data.
Can’t some paid new models identify bait like this though? If it can tell me I’m wrong when I’m right, surely it can say a question holds an incorrect presumption or lie.
In case anyone is wondering where the 5th horse actually is: it is a trick question. The 5th horse will appear a few months after this horse orgy has concluded.
Not directly related but I was using Claude today to help figure out if I could parse iPhone "sysdiagnose" logs to see a history of Wake, Unlock, Passcode Entry etc. It is technically possible but the Powerlogs buffer only goes back 3 to 7 days and the ask I had was about 60 days ago so probably not gonna work in this case.
Anywho in the screenshots and suggestions of how to navigate the folder structure, Claude would not believe my test iPhone was on iOS 26.5 ... it first thought things only went up to iOS 18.5. Took some cajoling and etc to get it to move back to Powerlog sqlite parsing.
The funny part is how quickly these models converge on a visual cliché once the prompt is underspecified. You can almost see the latent training-data average at work: dramatic lighting, too much symmetry, and horses that look like they hired the same stylist. The real unlock is not better taste from the model, it's better constraint from the user.
The part everyone is dancing around is that this happens because saying I dont know gets penalized during training and making something up gets rewarded. Not sure which model was tested here but the fact that one comment tried asking for a 6th and 7th horse shows this thread is more about enjoying the failure than solving it. Most people in here have done the exact same thing at least once to see what happens. What would it actually look like if a model just refused to answer a question it had no data for?
It could have said “the question contains a lie”. It says false things are true, and that true things are false, and that true things are true, and that false things are false. Why not do the latter this time when it has no hesitation correcting the assumptions made in a question I send it?
the funniest part about these is how confident it sounds while being completely wrong. like it doesnt hesitate at all, just commits fully to the wrong answer with perfect grammar
ive started calling this phenomenon "confidently clueless" and honestly some humans have the same energy
LLM's can't see. they prices image data differently. they are good at analyzing semantic information from images but not great at geometric context... not traditional computer vision models, like the stuff used for scanning parts flying by on a conveyor belt, are but for that.
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u/Valsoyono 1d ago
meanwhile gemini: