Unfair - human beats AI in this comparison, as human will instantly answer "I don't know" instead of yelling a random number.
Or at best "I don't know, but maybe I can find out" and proceed to finding out/ But he is unlikely to shout "6" because he heard this number once when someone talked about light.
Because LLMs dont have a textual representation of any text they consume. Its just vectors to them. Which is why they are so good at ignoring typos, the vector distance is so small it makes no difference to them.
what bothers me is not that this issue will certainly disappear now that it has been identified, but that that we have yet to identify the category of these "stupid" bugs ...
We already know exactly what causes these bugs. They are not a fundamental problem of LLMs, they are a problem of tokenizers. The actual model simply doesn't get to see the same text that you see. It can only infer this stuff from related info it was trained on. It's as if someone asked you how many 1s there are in the binary representation of this text. You'd also need to convert it first to think it through, or use some external tool, even though your computer never saw anything else.
