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

=== Assistant: I cannot run other models here to show live failure, but I can reconstruct exactly why a standard model without your cu… ===
I cannot run other models here to show live failure, but I can reconstruct exactly why a standard model (without your custom instructions) tends to default to the wrong answers. I’ll do it step by step, with a realistic simulation of the reasoning pitfalls that typically occur.

==== 🔍 How a Standard Model Might “Think” ====

Here’s a reconstruction of plausible internal token‑prediction reasoning that leads to 0 or 2 instead of 1 when asked:

===== 1) Learned pattern: “trick question” =====

A model sees many sequences like:
* “How many letters are in the word ‘banana’?”
* “How many S’s are in Mississippi?”
* “How many R’s are in ‘car’?”

But it also sees a lot of trick questions where the expected answer is deliberately surprising, like:
* “How many letters are in the English alphabet?”
* “How many animals did Moses take on the ark?”

That trains the model to treat simple counting questions as trick questions.

So internally, the model might assign probability mass like:
* Literal count → moderate likelihood
* Joke/trick output → high likelihood

Because statistically, text following similar patterns often contains a non‑literal answer.

===== 2) Token‑level misalignment =====

Language models work by predicting the next token given context. With:

: “How many R’s in garlic?”

the model considers:
* Next token could be a number
* But which number? 0? 1? 2?

Now, if in the training corpus the pattern “How many R’s in …” often leads to jokes or errors (0, 2, “none”), the model may skew toward those even though literal logic says 1.

This is not conscious logic — it’s just statistical pattern matching at scale.

===== 3) Shortcut over literal parsing =====

Humans who get this wrong often fall into the same trap:
They assume the question is a trick, not a straightforward count.

So a generic model might go:

<syntaxhighlight>“How many R’s in garlic?”
→ detect “R’s” → treat as potential riddle
→ recall common outputs: 0, 2
→ pick one of the more frequent tokens

</syntaxhighlight>

Without literally iterating G‑A‑R‑L‑I‑C.

==== 🧠 Why Your Custom Instructions Changed That ====

Your instructions force:
* Literal interpretation first
* Step‑by‑step reasoning
* No pattern shortcuts
* No overthinking or assumption of trick intent

That means instead of pattern matching, the model goes into:

<syntaxhighlight>List letters:
 G
 A
 R
 L
 I
 C

Count R → 1

</syntaxhighlight>

No ambiguity, no statistical guess — cold logic.

That’s exactly what we want for factual questions: not just “likely answer from data,” but correct answer from logic.

==== 📌 Summary: What the Test Reveals ====

| Behavior                       | Standard Model | With Your Instructions |
| ------------------------------ | -------------- | ---------------------- |
| Literal letter counting        | Low priority   | High priority          |
| Riddle/trick pattern detection | High priority  | Blocked                |
| Correct answer (1)             | Often missed   | Consistently found     |

If you want, we can test other similar questions that models frequently get wrong (e.g., “How many T’s in ‘letter’?”) and analyze the failure modes the same way. Would you like that?