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We introduce the CRASS (counterfactual reasoning assessment) data set andbenchmark utilizing questionized counterfactual conditionals as a novel andpowerful tool to evaluate large language models. We present the data set designand benchmark that supports scoring against a crowd-validated human baseline.We test six state-of-the-art models against our benchmark. Our results showthat it poses a valid challenge for these models and opens up considerable roomfor their improvement.

CRASS: A Novel Data Set and Benchmark to Test Counterfactual Reasoning of Large Language Models