@inproceedings{palta-etal-2024-plausibly,
    title = "Plausibly Problematic Questions in Multiple-Choice Benchmarks for Commonsense Reasoning",
    author = "Palta, Shramay  and
      Balepur, Nishant  and
      Rankel, Peter  and
      Wiegreffe, Sarah  and
      Carpuat, Marine  and
      Rudinger, Rachel",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.findings-emnlp.198/",
    doi = "10.18653/v1/2024.findings-emnlp.198",
    pages = "3451--3473",
    abstract = "Questions involving commonsense reasoning about everyday situations often admit many possible or plausible 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 most 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 accuracyand high variation in performance on the subset, suggesting our plausibility criterion may be helpful in identifying more reliable benchmark items for commonsense evaluation."
}