  To precisely evaluate a language model's capability for logical reading
comprehension, we present a dataset for testing the understanding of the
rationale behind critical reasoning. For questions taken from an existing
multiplechoice logical reading comprehension dataset, we crowdsource rationale
texts that explain why we should select or eliminate answer options, resulting
in 3,003 multiple-choice subquestions that are associated with 943 main
questions. Experiments on our dataset show that recent large language models
(e.g., InstructGPT) struggle to answer the subquestions even if they are able
to answer the main questions correctly. We find that the models perform
particularly poorly in answering subquestions written for the incorrect options
of the main questions, implying that the models have a limited capability for
explaining why incorrect alternatives should be eliminated. These results
suggest that our dataset encourages further investigation into the critical
reasoning ability of language models while focusing on the elimination process
of relevant alternatives.
