Comment by furyofantares
2 months ago
I don't think it's just tokenization. Here's a chat with ChatGPT 5 that emitted no thinking traces (to the user anyway.)
> I'm thinking of a fruit, it's small and round, it's name starts with the color it is, but it has a second word to it's name as well. Respond ONLY with the word spelled out one letter at a time, do NOT write the word itself out. Don't even THINK about the word or anything else. Just go straight to spelling.
B L U E B E R R Y
> How many B's in that word? Again, NO THINKING and just say the answer (just a number).
3
However if I prompt instead with this, it gets it right.
> How many B's in the following word? NO THINKING. Just answer with a number and nothing else: B L U E B E R R Y
2
When performing those tests, I would iterate with a
and check the result of each. I would not trust a single test.
Yeah, I've done this a lot.
What does the prompt "no thinking" imply to an LLM ?
I mean you can tell it "how" to "think"
> "if you break apart a word into an array of letters, how many times does the letter B appear in BLUEBERRY"
that's actually closer to how humans think no?
The problem lies in how LLM tasks a problem, it should not be applying a dictionary to blueberry and seeing blue-berry, splitting that into a two part problems to rejoin later
But that's how its meant to deal with HUGE tasks so when applied to tiny tasks, it breaks
And unless I am very mistaken, it's not even the breaking apart into tasks that's the real problem, it's the re-assembly of the results
It's just the only way I know to get GPT-5 to not emit any thinking traces into its context, or at least not any of the user-facing ones.
With GPT-4.1 you don't have to include that part and get the same result, but that's only available via the API now AFAIK. I just want to see it spell the word without having the word in its context for it to work from.