CRASS: A Novel Data Set and Benchmark to Test Counterfactual Reasoning of Large Language Models
International Conference on Language Resources and Evaluation(2021)
摘要
We introduce the CRASS (counterfactual reasoning assessment) data set and benchmark utilizing questionized counterfactual conditionals as a novel and powerful tool to evaluate large language models. We present the data set design and benchmark as well as the accompanying API that supports scoring against a crowd-validated human baseline. We test six state-of-the-art models against our benchmark. Our results show that it poses a valid challenge for these models and opens up considerable room for their improvement.
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