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To precisely evaluate a language model's capability for logical readingcomprehension, we present a dataset for testing the understanding of therationale behind critical reasoning. For questions taken from an existingmultiplechoice logical reading comprehension dataset, we crowdsource rationaletexts that explain why we should select or eliminate answer options, resultingin 3,003 multiple-choice subquestions that are associated with 943 mainquestions. Experiments on our dataset show that recent large language models(e.g., InstructGPT) struggle to answer the subquestions even if they are ableto answer the main questions correctly. We find that the models performparticularly poorly in answering subquestions written for the incorrect optionsof the main questions, implying that the models have a limited capability forexplaining why incorrect alternatives should be eliminated. These resultssuggest that our dataset encourages further investigation into the criticalreasoning ability of language models while focusing on the elimination processof relevant alternatives.

Evaluating the Rationale Understanding of Critical Reasoning in Logical Reading Comprehension