tw-exam-bench-sample

The 22,021 questions the opentwbench.ai leaderboard actually scores. A fixed, deterministic stratified sample of OpenTWBench/tw-exam-bench, so that a score you compute locally is comparable to a score on the board.

from datasets import load_dataset

ds = load_dataset("OpenTWBench/tw-exam-bench-sample", split="test")

Why this exists separately

The full suite is 255,854 questions. Running it for one model is a day on eight B200s; running it for the 107 models on the board is not a thing anyone will do. So the board scores a sample — and a sample is only useful if everyone is scored on the same one.

This dataset is that sample, published so it can be checked rather than trusted. Every model on the board answered these rows and no others.

How it was drawn

Per subject, the min(150, n) questions with the smallest sha256(question). No seed, no shuffle, no reference to difficulty — re-running the draw on the same corpus reproduces it exactly.

148 subjects x 150                      22,200 rows
less rows on the withdrawal list          -179
                                      ----------
                                        22,021 rows

The withdrawal list is the 2,180 items whose stems depend on a figure the published text does not carry. 61 subjects end up under 150 because of it; the smallest, electronics, has 123.

Because selection is by content hash, it is independent of whether any model answers correctly. A sample drawn this way can differ from the population only through composition, and that difference has been measured rather than assumed.

What a score on this set means

	Qwen3.8-27B
Full corpus, 253,674 items	82.19%
This sample, same per-subject accuracies re-weighted by its composition	80.1%

Equal weighting per subject moves weight off the large, high-scoring medical corpus and onto the small legal and administrative subjects, where models are weakest. So a score here sits about 2 points below what a full-corpus run would report.

That offset is constant and applies to every model, so rankings are unaffected. Scores on this set are comparable to each other. They are not comparable to a full-corpus number.

Fields

Field	Description
row_id	0–22,020, this dataset's canonical row order
qh	sha256(question)[:16] — the key the board's result files use. Not unique, see below
subject	Subject slug, matching the config names in tw-exam-bench
subject_zh	Full examination subject name (科目全名)
subject_group	Examination category (類科組別)
exam_level	Examination grade (等級分類)
exam_name	Official title of the examination
year_ce	Exam year, 2012–2026
paper_id / q_no	Originating paper and question number — together a unique key
question, A, B, C, D	Stem and option texts
answer	Gold option letter

The row shape is the Twinkle Eval MCQ contract; the provenance columns ride alongside and are ignored by the harness.

A trap worth knowing about

Taiwan's examinations reuse a stem verbatim across years with a fresh set of options. 下圖為何種通氣模式？ is six different questions in mechanical ventilation between 2014 and 2019, with three different answer keys.

In this sample, 119 stems stand for 247 separate items. So:

• qh collides. Keying your results on it discards 128 real questions.
• Score row-wise, over all 22,021 rows. Use row_id, or (paper_id, q_no).

Protocol

Scored with Twinkle Eval: temperature 0, shuffle_options: true, answer extracted from \boxed{}. Items a model never answers count as wrong rather than being dropped — the denominator is the dataset, not the subset that parsed.

Relationship to the other datasets

Dataset	Rows	Use
tw-exam-bench	255,854	the full suite, per-subject configs
tw-exam-bench-sample	22,021	what the leaderboard scores
tw-formosa-bench	1,696	everyday and contemporary Taiwan

Licence and source

Apache-2.0. Questions are open data published by Taiwan's Ministry of Examination (考選部).