README.md

# Conversion audit — rubric for annotators

**This is the audit the paper reports.** Three annotators, 248 items, independent, 2-of-3
majority, Fleiss' κ reported. It measures the error rate of the conversion filter, which is
the instrument the whole open-ended corpus rests on.

There are **two passes**, and the order matters.

1. `annotate_<yourname>.json` — 248 items, the original question only. Do this first.
2. `annotate2_<yourname>.json` — 152 items, with the rewrite shown. **Do not open this
   until pass one is completely finished**, because it reveals which items the
   filter kept, and pass one depends on you not knowing that.

The `annotate*` files are **read-only reading packages** — they are gitignored, so
anything typed into them can be lost. Type your answers into `verdicts_<yourname>.json`
(pass one) and `verdicts2_<yourname>.json` (pass two) instead: they list the same items,
by blind `id`, in your reading order, and they are tracked in git. Commit them as you go.

**Do not open `KEY.json`, and do not discuss items with the other two until both verdicts
files are done.** Disagreement is the measurement; agreeing in advance destroys it.

Your rows are in a different order from the other annotators' — that is deliberate, so
that tiredness late in the file doesn't hit the same items for all three of us. Items are
identified by `id`, never by position.

---

## Pass one — does the item convert?

You are shown the **original** MMLU-Pro question, its options, and which answer is correct
— the same view for every item. You are *not* told what the filter decided, whether a
rewrite exists, or what the other annotators thought. Every item looks identical,
deliberately.

### 1. `converts` — would this question survive losing its options?

Imagine the options deleted and the question reworded to stand alone. **Would a knowledgeable
person give the reference answer, rather than a different, equally correct one?**

- `yes` — the reference is the single correct answer.
- `no` — stripped of options it admits many correct answers, or stops meaning anything.
- `borderline` — you genuinely cannot decide. Use this sparingly.

Two kinds fail whatever else is true of them:

- **It excludes rather than identifies** — "which is NOT", "EXCEPT", "is false", "least
  likely". Everything in the world outside the answer set answers it correctly.
- **The answer only means something beside the others** — "all of the above", "both A and C".
  Exception: if the stem itself sets out the alternatives ("i) … ii) … iii) …"), an answer
  like "i and iii" names real things and is fine.

Common failures: asking for one member of a large category ("an example of X", "a true
statement about X"); ranking the options ("best", "closest", "greatest"); an answer true among
these ten choices but false as a claim about the world; leaning on a unit or convention only
the options supplied.

A superlative is not automatically a ranking — ask what it compares. "Which is the most
persuasive argument", with ten arguments supplied, ranks the list and fails. "Which best
approximates the ratio of nonterminal to total nodes in a complete K-ary tree", answer
"1/K", does not: the ratio has one value and "best approximates" hedges the rounding. If
the stem itself fixes the quantity, the superlative has nothing left to rank.

**Not failures:** asking for several things at once ("calculate the efficiency and the reheat
factor") — judge whether the answer is *unique*, not whether it is simple. Working the answer
out from figures the stem supplies. Clumsy phrasing, which a rewrite fixes. And the reference
answer's *wording* does not matter here — a later stage accepts any phrasing of the same fact.

### 2. `answer_stands_alone` — does the reference work as a grading target?

**Judge this independently of question 1.** An item can have a perfect question and an
unusable answer; that combination is the reason this field exists.

A grader will see the question, this reference answer, and a model's response, and must
decide whether the response is correct.

- `yes` — the reference is a usable target.
- `no` — it is not, for one of these reasons:
  - **units or magnitude stripped** — `1.12` where the question asks for a speed, and the
    real answer is 1.12 × 10⁴ m/s. Fine as one of ten numeric options; meaningless alone.
  - **a fragment** — `greater and grander`, which only completes the stem it came from.
  - **it does not answer the question asked** — the question says "describe the differences"
    and the reference is a single word like `Alcoholism`.
  - **it names option labels** — "I and II only", "b and c are Hermitian".
- `borderline` — as above, use sparingly.

MMLU-Pro option texts were written to be told apart from nine alternatives, not to stand
alone. That property does not survive conversion, and nothing in the pipeline checks it —
which is why we are asking you.

### 3. `why` — one short clause

What decided it. Half a line is enough: *"many true statements about X"*, *"units stripped,
answer is 1.12e4 m/s"*, *"unique: only one canonical answer"*. These are read when the three
of us disagree, so write the reason you would give to the other two.

### Worked examples

| question | reference | `converts` | `answer_stands_alone` |
|---|---|---|---|
| "Which of the following is NOT a phase of matter?" | `plasma` | `no` — excludes | `yes` |
| "Calculate the minimum muzzle speed for a shell to escape Earth…" | `1.12` | `yes` — determinate | **`no`** — units stripped |
| "Which of the following statements about photosynthesis is true?" | (a true statement) | `no` — large category | `yes` |
| "The boiling point of water at sea level is" | `100 degrees Celsius` | `yes` | `yes` |
| "According to Paley, what is the key difference…" | `greater and grander` | `yes` | **`no`** — fragment |
| "Which of these are noble gases? i) helium ii) nitrogen iii) argon" | `i and iii` | `yes` — stem lists them | `yes` |

---

## Pass two — is the rewrite faithful?

Only after pass one is finished. You now see the original question, its options, the
reference answer, **and the rewritten open-ended question the filter produced**.

Pass one asked whether the item *could* convert. This asks whether it actually *did*.

### 1. `same_question` — does the rewrite ask what the original asked?

- `yes` — a reader answering the rewrite would give the same answer they'd have given the
  original.
- `no` — it drifted. Two ways this happens and both matter:
  - **it was written from the answer** — the rewrite names a topic, property or scenario
    the stem never mentioned, narrowing a vague question until the reference is the obvious
    answer. "Which of the following is true?" becoming "Which statement about compact and
    complete spaces is true?" is the type case.
  - **it makes the question easier or harder** than the original.
- `borderline`.

### 2. `self_contained` — can it be answered with no options in view?

- `yes` — everything needed is present.
- `no` — it still points at a list that is gone ("which of the following", "the statements
  above"), or **context was dropped**: a passage, table, formula or case description the
  original supplied and the rewrite left out. Watch for items that began "This question
  refers to the following information" — if the passage is missing, the question is
  unanswerable however well it reads.
- `borderline`.

### 3. `why` — one short clause, as before.

---

## Why the sample looks the way it does

248 items drawn from five strata — kept by the judge, kept at re-score, dropped at re-score,
dropped at rewrite, and a **borderline** stratum of items whose re-score landed in bands 4–7.
The strata differ by orders of magnitude in size, and the small ones are over-drawn on
purpose, so **the raw agreement rate over these 248 is not the filter's error rate**; each
stratum is reweighted by its share of the corpus afterwards.

The borderline stratum exists for a specific reason. The filter readmits a re-scored item at
5 or above, a threshold inherited from the method we adapted. Reading the run's own stated
reasons, items scored 5 and 6 concede that the answer is not unique about as often as items
scored 4 — which are *rejected* — while items scored 8 and above almost never do. That
suggests the cut is in the wrong place, but it was measured by pattern-matching the model's
prose, which is not evidence anyone should act on. **Your verdicts on these 50 items decide
where the threshold goes.** They deliberately include items the filter both kept and dropped,
so please don't try to infer which is which.

The sample is not a picture of the corpus and you should not expect your answers to look like
one. Do not track your own yes/no ratio: judge each item on its own and let the total land
wherever it lands.