Turkish MMLU Pro: Traceable Option Augmentation and Its Validity Limits in Turkish Multiple-Choice Evaluation
cs.CL · cs.AI · cs.PF
Quick verdict
Augments 12,000 Turkish MCQs with borrowed options, shows accuracy drops, and candidly maps validity limits; a traceable resource, not a validated benchmark.
Summary
The paper constructs Turkish MMLU Pro, a 12,000-question Turkish multiple-choice benchmark in which each question keeps its stem, five original options and source key, and gains five options borrowed from other questions in the same 58-section corpus. Borrowed options are proposed by sentence-embedding retrieval and selected by a local language model under forced choice; deterministic verification reconstructs all 60,000 additions. A 100-question pilot calibrates 25 local models and the scoring protocol; five complete 12,000-question runs yield source-key accuracies of 34.84-81.38%. In the central paired experiment, an API model drops from 93.68% to 83.08% on 981 questions shared with the five-choice TR-MMLU (McNemar p~5e-23), and 102 of 115 lost answers select borrowed options. Accuracy falls 24.4 points on heuristic-flagged negative stems versus 5.9 points elsewhere, supporting a multiple-defensible-answer mechanism. A 200-question human audit records 47 and 31 multiple-answer judgments (R1/R2) with 25 unresolved items, but cross-reviewer reconciliation and undocumented reviewer tool use preclude independent validity estimates. The author is explicit that order and label changes confound the paired comparison, that the resource is an instrument for studying augmentation failure rather than a validated ten-choice benchmark, and that the dataset remains gated with an unresolved license conflict.
Review complete
Assessment at a glance
5
risk flags identified
● high 0
● medium 3
● low 2
5 strengths · 5 weaknesses · 5 author questions
AI text signal = Binoculars zero-shot detector score over 20 sampled passages (Fast-DetectGPT co-reported) — absence does not prove human authorship, and a flag does not imply misconduct.
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Key claims under review
- A traceable pipeline augments 12,000 Turkish-source questions to ten options, with all 60,000 borrowed additions deterministically reconstructible from pinned provenance.
- On 981 questions shared with TR-MMLU, adding borrowed options cuts one API model's source-key accuracy from 93.68% to 83.08% (exact McNemar p=5.27e-23), with 102 of 115 losses selecting borrowed options.
- The loss concentrates on heuristically flagged negative stems (-24.4 vs -5.9 percentage points), consistent with borrowed options creating additional defensible answers.
- A completed 200-question human-checked audit records multiple-answer judgments (47 in R1, 31 in R2, 25 unresolved) but its dependence and unverified reviewer tool use preclude a pooled validity estimate.
- The authors explicitly claim the artifact as an instrument for studying augmentation failure, not a validated ten-choice benchmark, and propose the scores not be used for ranking models.
Strengths
End-to-end traceability of the augmentation
Pinned source revision (30c94d45...), SHA-256-derived question IDs, and deterministic reconstruction of all 12,000 records and 60,000 additions (§3.3), with exact hashes and regeneration scripts documented in Appendix G.
Statistical hygiene unusual for benchmark papers
95% Wilson CIs throughout, paired Wald intervals plus exact McNemar (p=5.27e-23) for the 981-question comparison, Bonferroni threshold (0.005) across ten exploratory pilot tests, and a section-level cluster bootstrap sensitivity interval [-12.89, -8.36] (§4.2, App. G); ranking inference explicitly refused.
Mechanistic, quantified failure-mode analysis
Exemplary transparency about its own weaknesses
Rigorous scorer separation
Weaknesses
Confounded headline comparison
Weak audit evidence for the validity concern
Single-model paired evidence on a proprietary API
Resource not practically accessible
Aggregate borrowed-option evidence is thin
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Scope & Claims
Turkish MMLU Pro (arXiv:2609.15467) is a resource-plus-analysis paper: it augments 12,000 Turkish-source multiple-choice questions to ten options by borrowing five distractors from other questions in the same section, then studies whether the augmentation preserves assessment validity. The stated contribution is deliberately narrow - a traceable construction and an analysis of its validity limits - with the author explicitly disclaiming that lower ten-choice scores measure knowledge better and asking that the scores not be used to rank models (§1, §6). Claims are modest, well-hedged, and mostly earned by the evidence presented.
The remaining sections cover Methodology Assessment, Statistical Evidence, Results & Reproducibility, Limitations & Risks, and Verdict & Recommendations.
- All 5 strengths & 5 weaknesses, analyzed in detail
- 5 risk flags with severity, section & reviewer questions
- Methods & statistical evidence assessment
- 5 questions a peer reviewer would ask
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Frequently asked questions
What is Turkish MMLU Pro?
A 12,000-question Turkish multiple-choice resource in which every question keeps its original five options and answer key and gains five options borrowed from other questions in the same subject section, giving ten options across 58 sections. It is released as a traceable instrument for studying how option augmentation affects evaluation validity, not as a validated leaderboard benchmark.
Does adding more answer options make language models score lower?
In this study's setting, yes: one API-served model's accuracy on 981 questions fell from 93.68% with five options to 83.08% with ten. However, the drop confounds option count with changed option order, letter labels and prompt length, and the paper explicitly does not claim that ten-choice scores measure knowledge better.
Why are negative questions like 'which is NOT...' a problem in this benchmark?
For negative stems, a borrowed option from another question can also satisfy the requested exception, leaving two defensible answers under a single-key scorer. Heuristically flagged negative questions lost 24.4 accuracy points in the paired comparison versus 5.9 points on other questions.
Is the Turkish MMLU Pro dataset publicly available?
A pinned snapshot exists on Hugging Face but is manually gated, so file access requires approval. The completed manifest, model responses, provenance records and audit packet are not publicly archived, and the source collection carries an unresolved license conflict (CC BY-NC-ND vs CC BY-NC labels) with third-party material.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Tables were extracted as flattened text; some column alignments (e.g., the 25-row pilot table in Appendix E) were reconstructed from surrounding prose, so minor value misreadings are possible.
- No figures appear in the extraction; the paper may contain none, and footnote text (e.g., footnote 0 on AI use) was inline and could be partially garbled.
- Repository state (gating, license labels) is taken from the paper's own 12 September 2026 account and was not externally verified.
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