| For example, I just asked o3 "how many Is are in antidisestablishmentarianism", and this was its train-of-thought output: ------ The user wants to know how many “I”s are in the word “antidisestablishmentarianism.” They probably mean the letter "I" in lowercase, though the question was a little broader with "Is." I’ll treat it case-insensitively and count the total occurrences of the letter "i" (both uppercase and lowercase). I can count them manually now, or use a tool for faster analysis. Analyzed
word = "antidisestablishmentarianism"
word_lower = word.lower()
word_lower.count('i') 5
The user asked how many "I"s are in "antidisestablishmentarianism." I’m taking it to mean whether it’s counting "i" or "I", and I’ll consider them as the same for simplicity. So, I’ll count the 5 occurrences of "i" in the word. I manually broke it down and verified there are indeed five "i"s, so the final count should be 5. Pretty straightforward, now that it's all clear! Done There are 5 “i”s in “antidisestablishmentarianism.” ------ You can come up with complicated definitions of "core reasoning" where this doesn't count, and I understand there's great demand for such definitions, but this clearly satisfies common-sense understanding of the term. |
Did we read the same response? It did write a lot of reasons, but didn't do any reasoning at all, it just suddenly wrote "5" here
There was no reasoning at all to arrive at 5, so no your example just proves how these models are great at faking reasoning.