mlsanityData to 14 Oct 2026

Turkish MMLU Pro: Traceable Option Augmentation and Its Validity Limits in Turkish Multiple-Choice Evaluation

arXiv 2609.15467 ↗First posted 14 Sep 2026cs.CL cs.AI cs.PFcited by 0

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Abstract (arXiv)

Adding answer options can lower multiple-choice scores without improving assessment validity. Turkish MMLU Pro examines this distinction using 12,000 Turkish-source questions across 58 sections. Each question retains its stem, five original options and source key, and receives five options copied from other questions in the same section. Sentence-embedding retrieval proposes candidates; a language model selects existing identifiers. Deterministic verification reconstructs all 60,000 additions. A 25-model calibration exposes scoring and generation-budget effects. Five evaluations produce source-key accuracies of 34.8%-81.4%. On 981 shared questions, one API-served model falls from 93.7% with five choices to 83.1% with ten; 102 of 115 lost correct responses select borrowed options. The decrease is 24.4 percentage points on heuristically flagged negative stems and 5.9 points elsewhere. A completed human-checked audit of 200 sampled questions, with undocumented reviewer tool use, yields 47 and 31 multiple-answer judgments across the two record sets, 25 of the latter unresolved. These records support concern about ambiguity, while their dependence and incomplete reviewer-method documentation limit validation. Because order and labels also change, the paired comparison measures augmentation as implemented. The contribution is a traceable construction and an analysis of its validity limits, not evidence that lower ten-choice scores measure knowledge better.

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References · 15

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2024MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmarkcited by 1,848
  3. 2018Datasheets for Datasetscited by 1,386
  4. 2023Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questionscited by 263
  5. 2024Are We Done with MMLU?cited by 194
  6. 2024"My Answer is C": First-Token Probabilities Do Not Match Text Answers in Instruction-Tuned Language Modelscited by 69
  7. 2024TurkishMMLU: Measuring Massive Multitask Language Understanding in Turkishcited by 32
  8. 2024Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluationcited by 25
  9. 2024Setting Standards in Turkish NLP: TR-MMLU for Large Language Model Evaluationcited by 11
  10. 2025TUMLU: A Unified and Native Language Understanding Benchmark for Turkic Languagescited by 9
  11. 2025Cetvel: A Unified Benchmark for Evaluating Language Understanding, Generation and Cultural Capacity of LLMs for Turkishcited by 8
  12. 2026TurkBench: A Benchmark for Evaluating Turkish Large Language Modelscited by 5
3 more
  1. 2023Assessing Distractors in Multiple-Choice Testscited by 3
  2. 2025B"uy"uk Dil Modelleri iccin TR-MMLU Benchmarki: Performans Deugerlendirmesi, Zorluklar ve .Iyilecstirme Firsatlaricited by 2
  3. 2026Pushing the Boundaries of Multiple Choice Evaluation to One Hundred Optionscited by 1

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