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EMNLP 2024emnlpfindings

Plausibly Problematic Questions in Multiple-Choice Benchmarks for Commonsense Reasoning

Shramay Palta, Nishant Balepur, Peter Rankel, Sarah Wiegreffe, Marine Carpuat, Rachel Rudinger

University of Maryland · Allen Institute for AI

PDF 由论文原始站点提供,PaperCompass 不保存论文文件。DOI 10.18653/v1/2024.findings-emnlp.198 ↗

摘要

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 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.