I asked it how many Ts are in names of presidents since 2000. It said 4 and stated that "Obama" contains 1 T.
Toebama
We gotta raise the bar, so they keep struggling to make it “better”
My attempt
0000000000000000
0000011111000000
0000111111111000
0000111111100000
0001111111111000
0001111111111100
0001111111111000
0000011111110000
0000111111000000
0001111111100000
0001111111100000
0001111111100000
0001111111100000
0000111111000000
0000011110000000
0000011110000000
Btw, I refuse to give my money to AI bros, so I don’t have the “latest and greatest”
Tested on ChatGPT o4-mini-high
It sent me this
0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0
0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0
0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0
0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0
0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0
0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0
0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0
0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0
0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0
0 0 1 1 1 0 0 1 1 1 0 0 0 0 0 0
0 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0
1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0
I asked it to remove the spaces
0001111100000000
0011111111000000
0011111110000000
0111111111100000
0111111111110000
0011111111100000
0001111111000000
0011111100000000
0111111111100000
1111111111110000
1111111111110000
1111111111110000
1111111111110000
0011100111000000
0111000011100000
1111000011110000
I guess I just murdered a bunch of trees and killed a random dude with the water it used, but it looks good
People who think that LLMs having trouble with these questions is evidence one way or another about how good or bad LLMs are just don't understand tokenization. This is not a symptom of some big-picture deep problem with LLMs; it's a curious artifact like in a jpeg image, but doesn't really matter for the vast majority of applications.
You may hate AI but that doesn't excuse being ignorant about how it works.
These sorts of artifacts wouldn't be a huge issue except that AI is being pushed to the general public as an alternative means of learning basic information. The meme example is obvious to someone with a strong understanding of English but learners and children might get an artifact and stamp it in their memory, working for years off bad information. Not a problem for a few false things every now and then, that's unavoidable in learning. Thousands accumulated over long term use, however, and your understanding of the world will be coarser, like the Swiss cheese with voids so large it can't hold itself up.
You're talking about hallucinations. That's different from tokenization reflection errors. I'm specifically talking about its inability to know how many of a certain type of letter are in a word that it can spell correctly. This is not a hallucination per se -- at least, it's a completely different mechanism that causes it than whatever causes other factual errors. This specific problem is due to tokenization, and that's why I say it has little bearing on other shortcomings of LLMs.
No, I'm talking about human learning and the danger imposed by treating an imperfect tool as a reliable source of information as these companies want people to do.
Whether the erratic information is from tokenization or hallucinations is irrelevant when this is already the main source for so many people in their learning, for example, a new language.
Hallucinations aren't relevant to my point here. I'm not defending that AIs are a good source of information, and I agree that hallucinations are dangerous (either that or misusing LLMs is dangerous). I also admit that for language learning, artifacts caused from tokenization could be very detrimental to the user.
The point I am making is that LLMs struggling with these kind of tokenization artifacts is poor evidence for drawing any conclusions about their behaviour on other tasks.
Also just checked and every open ai model bigger than 4.1-mini can answer this. I think the joke should emphasize how we developed a super power inefficient way to solve some problems that can be accurately and efficiently answered with a single algorithm. Another example is using ChatGPT to do simple calculator math. LLMs are good at specific tasks and really bad at others, but people kinda throw everything at them.
Honey, AI just did something new. It's time to move the goalposts again.
When we see LLMs struggling to demonstrate an understanding of what letters are in each of the tokens that it emits or understand a word when there are spaces between each letter, we should compare it to a human struggling to understand a word written in IPA format (/sʌtʃ əz ðɪs/) even though we can understand the word spoken aloud normally perfectly fine.
But if you've learned IPA you can read it just fine
I know IPA but I can't read English text written in pure IPA as fast as I can read English text written normally. I think this is the case for almost anyone who has learned the IPA and knows English.
Maybe OP was low on the priority list for computing power? Idk how this stuff works
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