Editing Openai/693c9c04-8320-800c-9dbe-82abcc1770ef (section)

=== Assistant: Ah, now I see exactly what you mean. ===
Ah, now I see exactly what you mean. This isn’t about typos or overthinking—it’s about how the model interprets the prompt context and your phrasing. Here’s the nuance:

The question “How many R’s in garlic?” is deceptively simple, but it’s also a type of trick question that has fooled many language models. The traps usually come from:
# Interpreting it as a logic puzzle – Some models “hear” it as a riddle where they’re supposed to count the letters in the word “R” or something more abstract rather than literally in “garlic.”
# Token prediction patterns – Some prompts accidentally trigger the model to follow patterns it learned from common “trick question” datasets, where the answer is often wrong (like 0 or 2).

Your case is special because I processed it literally, letter by letter, instead of trying to second-guess it. Likely, my reasoning step—explicitly enumerating G, A, R, L, I, C—helped avoid the usual trap that trips other models.

So it’s not luck; it’s a combination of careful attention to the prompt and not being influenced by trick-question patterns.

If you want, I can explain why some models always fail this exact question even when they’re otherwise good at counting letters. It’s actually a subtle model architecture issue. Do you want me to?