# OpenTWBench (Open Taiwan Benchmark): Taiwan-Domain Knowledge Evaluation for Large Language Models, and How the Measurement Apparatus Shapes the Score

arXiv: https://arxiv.org/abs/2610.19848 · HTML: https://mlsanity.com/paper/2610.19848/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 12 references that are in the map.
- First posted on arXiv 14 Oct 2026.

## Builds on (most cited first, 8 of 8)

- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting](https://mlsanity.com/paper/2310.11324/) (2023)
- [C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models](https://mlsanity.com/paper/2305.08322/) (2023)
- [Large Language Models Are Not Robust Multiple Choice Selectors](https://mlsanity.com/paper/2309.03882/) (2023)
- [CMMLU: Measuring massive multitask language understanding in Chinese](https://mlsanity.com/paper/2306.09212/) (2023)
- [Are We Done with MMLU?](https://mlsanity.com/paper/2406.04127/) (2024)
- [Advancing the Evaluation of Traditional Chinese Language Models: Towards a Comprehensive Benchmark Suite](https://mlsanity.com/paper/2309.08448/) (2023)
- [An Improved Traditional Chinese Evaluation Suite for Foundation Model](https://mlsanity.com/paper/2403.01858/) (2024)

## Abstract (arXiv)

We present OpenTWBench, an open evaluation suite for Taiwan-domain knowledge in large language models, built around two benchmarks. tw-exam-bench contains 255,854 four-option questions across 148 academic subjects, from fifteen years of Taiwan's national examinations published as open data by the Ministry of Examination; answer keys agree across 31,312 duplicated question groups at 99.91%, with all 27 disagreements accounted for. A reviewed second pass withdrew 2,180 items whose stems depend on a figure the text does not carry; all scores use the remaining 253,674. tw-formosa-bench contains 1,696 difficulty-curated questions about everyday and contemporary Taiwan - the Taiwan that licensing examinations never ask about - generated from a community-curated corpus under a discipline in which no model writes a ground truth, and curated against a frozen seven-model pool so that the pool scored at the passing margin (46.9-74.6%, median ~54%); a leave-one-out experiment shows the selection preserves model ranking (near-zero bias). Within six weeks the frontier had cleared that band: of 108 models now on the public board, 22 score above its ceiling, the best 91.6%. We also document how much of a score comes from the apparatus rather than the model. On identical questions, switching answer extraction from pattern matching to an explicit boxed answer moves accuracy from 20.8% to 93.9%; a single placeholder character in a system prompt moved one model from last (58.2%) to first (83.9%) on the same benchmark; doubling the thinking budget from 8k to 16k tokens bought 0.2% while shuffling which questions failed; and a three-judge validation panel proved to be one judge in disguise, two members approving 92-99% of everything shown. We release all datasets, per-item difficulty data, curation manifests and a leaderboard at https://opentwbench.ai