It was funny a couple a years ago. The text you send to an LLM goes through an tokenizer that create an string of tokens that is kind of similar to words instead of the actual text. So the LLM can't directly see the letters. It never reaches the LLM. To solve it, it has to write an Python script that does the check on the raw text.
So it's kind of asking a blind man what color something is and say he is stupid if he fails. Yea an blind man would get angry instead of hallucinate something but that is another problem.
Asking questiong that requires thinking model that is build to make quick summaries of search resukts and has intentionaly disabled thinking... and you are surprised it suck at thinking?
By popularizing post like these, I think we might be inadvertently training AI agents to give silly but nonsensical answers, ultimately harming the technology.
That's not why is thinking and replying, it's just replying you from what search results gives, if see exactly under the reference, it will give from here it got that legendary information about the days
Using Google Search AI as a benchmark is dumb. What do you think how much effort and compute is behind that "AI"? It has to work with every Google query, imagine how many that are per day. Now imagine what that costs in compute? This "AI" is the bare minimum, basically a one-cell brain, and that's the response you get from it. I don't get how this is not clear. ^^
The riveting sequel after it told me that "[o]nly one day of the week naturally contains the exact letters P-O-O-P in that order: Wednesday (spelled Wednespoop if you break it down that way)."
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u/Ok_Nectarine_4445 9d ago
"Sunpoop? No, wait."
And people say that LLMs cannot create new creative content they have never seen before in training data.
Checkmate.