Plausibly Problematic Questions in Multiple-Choice Benchmarks for Commonsense Reasoning
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
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- 2022Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanationscited by 159
- 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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- 2025Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Abovecited by 52
- 2025BLAB: Brutally Long Audio Benchcited by 11
- 2024Susu Box or Piggy Bank: Assessing Cultural Commonsense Knowledge between Ghana and the U.Scited by 9
- 2025Garbage In, Reasoning Out? Why Benchmark Scores are Unreliable and What to Do About Itcited by 5
- 2026BenchMarker: An Education-Inspired Toolkit for Highlighting Flaws in Multiple-Choice Benchmarkscited by 5
- 2025A large-scale evaluation of commonsense knowledge in humans and large language modelscited by 4
- 2025Social Story Frames: Contextual Reasoning about Narrative Intent and Receptioncited by 3
- 2025Arguments that Alter Minds: LLM Rationales Sway Human (and LLM) Notions of Plausibilitycited by 2
- 2025Speaking the Right Language: The Impact of Expertise Alignment in User-AI Interactionscited by 1
- 2025The Dog the Cat Chased Stumped the Model: Measuring When Language Models Abandon Structure for Shortcutscited by 0
- 2026BAGEL: Benchmarking Animal Knowledge Expertise in Language Modelscited by 0
- 2026Benchmarking the Benchmarks: Testing the Predictive Validity of Commonsense Benchmarkscited by 0
- 2026Check The Scoreboard: An Analysis of Scoring Schemes on Multiple-Choice Evaluation30 Aug 2026
- 2026Benchmarking the Benchmarks: Testing the Predictive Validity of Commonsense Benchmarks4 Aug 2026
- 2026BAGEL: Benchmarking Animal Knowledge Expertise in Language Models17 Apr 2026
- 2026BenchMarker: An Education-Inspired Toolkit for Highlighting Flaws in Multiple-Choice Benchmarks5 Feb 2026
- 2025Social Story Frames: Contextual Reasoning about Narrative Intent and Reception17 Dec 2025
- 2025The Dog the Cat Chased Stumped the Model: Measuring When Language Models Abandon Structure for Shortcuts23 Oct 2025
- 2025Arguments that Alter Minds: LLM Rationales Sway Human (and LLM) Notions of Plausibility9 Oct 2025
- 2025Garbage In, Reasoning Out? Why Benchmark Scores are Unreliable and What to Do About It30 Jun 2025
- 2025A large-scale evaluation of commonsense knowledge in humans and large language models15 May 2025
- 2025BLAB: Brutally Long Audio Bench5 May 2025
- 2025Speaking the Right Language: The Impact of Expertise Alignment in User-AI Interactions25 Feb 2025
- 2025Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above19 Feb 2025
- 2025Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Abovecited by 52
- 2025BLAB: Brutally Long Audio Benchcited by 11
- 2024Susu Box or Piggy Bank: Assessing Cultural Commonsense Knowledge between Ghana and the U.Scited by 9
- 2026BenchMarker: An Education-Inspired Toolkit for Highlighting Flaws in Multiple-Choice Benchmarkscited by 5
- 2025A large-scale evaluation of commonsense knowledge in humans and large language modelscited by 4
- 2025Social Story Frames: Contextual Reasoning about Narrative Intent and Receptioncited by 3
- 2025Arguments that Alter Minds: LLM Rationales Sway Human (and LLM) Notions of Plausibilitycited by 2
- 2025Speaking the Right Language: The Impact of Expertise Alignment in User-AI Interactionscited by 1
- 2025The Dog the Cat Chased Stumped the Model: Measuring When Language Models Abandon Structure for Shortcutscited by 0
- 2026BAGEL: Benchmarking Animal Knowledge Expertise in Language Modelscited by 0
- 2026Benchmarking the Benchmarks: Testing the Predictive Validity of Commonsense Benchmarkscited by 0
- 2026Check The Scoreboard: An Analysis of Scoring Schemes on Multiple-Choice Evaluationcited by 0
- 2026Check The Scoreboard: An Analysis of Scoring Schemes on Multiple-Choice Evaluation30 Aug 2026
- 2026Benchmarking the Benchmarks: Testing the Predictive Validity of Commonsense Benchmarks4 Aug 2026
- 2026BAGEL: Benchmarking Animal Knowledge Expertise in Language Models17 Apr 2026
- 2026BenchMarker: An Education-Inspired Toolkit for Highlighting Flaws in Multiple-Choice Benchmarks5 Feb 2026
- 2025Social Story Frames: Contextual Reasoning about Narrative Intent and Reception17 Dec 2025
- 2025The Dog the Cat Chased Stumped the Model: Measuring When Language Models Abandon Structure for Shortcuts23 Oct 2025
- 2025Arguments that Alter Minds: LLM Rationales Sway Human (and LLM) Notions of Plausibility9 Oct 2025
- 2025A large-scale evaluation of commonsense knowledge in humans and large language models15 May 2025
- 2025BLAB: Brutally Long Audio Bench5 May 2025
- 2025Speaking the Right Language: The Impact of Expertise Alignment in User-AI Interactions25 Feb 2025
- 2025Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above19 Feb 2025
- 2024Susu Box or Piggy Bank: Assessing Cultural Commonsense Knowledge between Ghana and the U.S21 Oct 2024
Similar, not cited
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- 2026LOGICAL-COMMONSENSEQA: A Benchmark for Logical Commonsense Reasoning
- 2025Who Benchmarks the Benchmarks? Towards Comprehensive Evaluation of Commonsense Reasoning Benchmarks
- 2025HellaSwag-Pro: A Large-Scale Bilingual Benchmark for Evaluating the Robustness of LLMs in Commonsense Reasoning
- 2022A Theoretically Grounded Benchmark for Evaluating Machine Commonsense
- 2025ReTraceQA: Evaluating Reasoning Traces of Small Language Models in Commonsense Question Answering
- 2025Reasoning Models are Test Exploiters: Rethinking Multiple-Choice
- 2025More Bias, Less Bias: BiasPrompting for Enhanced Multiple-Choice Question Answering
- 2026Benchmarking LLM Competence on Logical Inference over Probability Operators