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Changing Answer Order Can Decrease MMLU Accuracy

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arxiv 2406.19470 v2 pith:KGGC6CAI submitted 2024-06-27 cs.CL

Vipul Gupta , David Pantoja , Candace Ross , Adina Williams , Megan Ung This is my paper · ORCID
classification cs.CL
keywords accuracymodelmodelsmmluanswerdecreasemultipleorder
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As large language models (LLMs) have grown in prevalence, particular benchmarks have become essential for the evaluation of these models and for understanding model capabilities. Most commonly, we use test accuracy averaged across multiple subtasks in order to rank models on leaderboards, to determine which model is best for our purposes. In this paper, we investigate the robustness of the accuracy measurement on a widely used multiple choice question answering dataset, MMLU. When shuffling the answer label contents, we find that all explored models decrease in accuracy on MMLU, but not every model is equally sensitive. These findings suggest a possible adjustment to the standard practice of leaderboard testing, where we additionally consider the percentage of examples each model answers correctly by random chance.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Syntax Without Semantics: Teaching Large Language Models to Code in an Unseen Language

    cs.CL 2026-05 unverdicted novelty 7.0 of 10

    Fine-tuning LLMs on an unseen language teaches syntax but fails to transfer semantic competence, leaving Python with up to a 19% performance advantage and no tested intervention closing the gap.

  2. RoToR: Towards More Reliable Responses for Order-Invariant Inputs

    cs.CL 2025-02 conditional novelty 6.0 of 10

    RoToR makes a frozen LLM order-invariant by circularly rotating a single global sort of segment position IDs, and Selective Routing combines it with the original model for mixed lists.

  3. Benchmarking the Pedagogical Knowledge of Large Language Models

    cs.CL 2025-06 conditional novelty 5.0 of 10

    The authors release an open benchmark of 1,143 pedagogical knowledge questions from Chilean teacher exams and report accuracy, cost, and size trade-offs for 97 large language models.

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