CRoW: Benchmarking Commonsense Reasoning in Real-World Tasks

Mete Ismayilzada; Debjit Paul; Syrielle Montariol; Mor Geva; Antoine Bosselut

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arXiv · 2310.15239

CRoW: Benchmarking Commonsense Reasoning in Real-World Tasks

Mete Ismayilzada·Debjit Paul·Syrielle Montariol·Mor Geva·Antoine Bosselut

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Abstract

Recent efforts in natural language processing (NLP) commonsense reasoning research have yielded a considerable number of new datasets and benchmarks. However, most of these datasets formulate commonsense reasoning challenges in artificial scenarios that are not reflective of the tasks which real-world NLP systems are designed to solve. In this work, we present CRoW, a manually-curated, multi-task benchmark that evaluates the ability of models to apply commonsense reasoning in the context of six real-world NLP tasks. CRoW is constructed using a multi-stage data collection pipeline that rewrites examples from existing datasets using commonsense-violating perturbations. We use CRoW to study how NLP systems perform across different dimensions of commonsense knowledge, such as physical, temporal, and social reasoning. We find a significant performance gap when NLP systems are evaluated on CRoW compared to humans, showcasing that commonsense reasoning is far from being solved in real-world task settings. We make our dataset and leaderboard available to the research community at https://github.com/mismayil/crow.

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Mete Ismayilzada, Debjit Paul, Syrielle Montariol, Mor Geva, Antoine Bosselut. 2023-10-23. CRoW: Benchmarking Commonsense Reasoning in Real-World Tasks. https://arxiv.org/abs/2310.15239

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