mlsanityData to 14 Oct 2026

Plausibly Problematic Questions in Multiple-Choice Benchmarks for Commonsense Reasoning

arXiv 2410.10854 ↗First posted 6 Oct 2024cs.CL cs.AIcited by 13

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

Questions involving commonsense reasoning about everyday situations often admit many textitpossible or textitplausible answers. In contrast, multiple-choice question (MCQ) benchmarks for commonsense reasoning require a hard selection of a single correct answer, which, in principle, should represent the textitmost plausible answer choice. On 250 MCQ items sampled from two commonsense reasoning benchmarks, we collect 5,000 independent plausibility judgments on answer choices. We find that for over 20% of the sampled MCQs, the answer choice rated most plausible does not match the benchmark gold answers; upon manual inspection, we confirm that this subset exhibits higher rates of problems like ambiguity or semantic mismatch between question and answer choices. Experiments with LLMs reveal low accuracy and high variation in performance on the subset, suggesting our plausibility criterion may be helpful in identifying more reliable benchmark items for commonsense evaluation.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 6 of its 38 references that are in the map. First posted on arXiv 6 Oct 2024.

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6 of 38 references.

References · 6

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  3. 2024Mixtral of Expertscited by 1,823
  4. 2024Yi: Open Foundation Models by 01.AIcited by 719
  5. 2022Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanationscited by 159
  6. 2021Rissanen Data Analysis: Examining Dataset Characteristics via Description Lengthcited by 22

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Citing papers per year: 2024: 1; 2025: 8; 2026: 4.

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  1. 2026LOGICAL-COMMONSENSEQA: A Benchmark for Logical Commonsense Reasoning
  2. 2025Who Benchmarks the Benchmarks? Towards Comprehensive Evaluation of Commonsense Reasoning Benchmarks
  3. 2025HellaSwag-Pro: A Large-Scale Bilingual Benchmark for Evaluating the Robustness of LLMs in Commonsense Reasoning
  4. 2022A Theoretically Grounded Benchmark for Evaluating Machine Commonsense
  5. 2025ReTraceQA: Evaluating Reasoning Traces of Small Language Models in Commonsense Question Answering
  6. 2025Reasoning Models are Test Exploiters: Rethinking Multiple-Choice
  7. 2025More Bias, Less Bias: BiasPrompting for Enhanced Multiple-Choice Question Answering
  8. 2026Benchmarking LLM Competence on Logical Inference over Probability Operators