Plausibly Problematic Questions in Multiple-Choice Benchmarks for Commonsense Reasoning

Shramay Palta, Nishant Balepur, Peter Rankel, Sarah Wiegreffe, Marine Carpuat +1 more
10/6/2024
cs.CLcs.AI

No official code found. Best implementation: shramay-palta/commonsense-mcq-plausibility (1 star, MIT, last commit 2026-04-09).

Abstract

Questions involving commonsense reasoning about everyday situations often admit many $\textit{possible}$ or $\textit{plausible}$ 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 $\textit{most}$ 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 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.

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MIT

Repository for the data in the paper "Plausibly Problematic Questions in Multiple-Choice Benchmarks for Commonsense Reasoning" presented at EMNLP 2024

10Sep 10, 20246 months agoMIT

Cite this paper

@article{palta2024plausibly,
  title  = {Plausibly Problematic Questions in Multiple-Choice Benchmarks for Commonsense Reasoning},
  author = {Shramay Palta and Nishant Balepur and Peter Rankel and Sarah Wiegreffe and Marine Carpuat and Rachel Rudinger},
  year   = {2024},
  eprint = {2410.10854},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2410.10854v1}
}

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