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We introduce ZeroSCROLLS, a zero-shot benchmark for natural languageunderstanding over long texts, which contains only test and small validationsets, without training data. We adapt six tasks from the SCROLLS benchmark, andadd four new datasets, including two novel information fusing tasks, such asaggregating the percentage of positive reviews. Using ZeroSCROLLS, we conduct acomprehensive evaluation of both open-source and closed large language models,finding that Claude outperforms ChatGPT, and that GPT-4 achieves the highestaverage score. However, there is still room for improvement on multiple openchallenges in ZeroSCROLLS, such as aggregation tasks, where models struggle topass the naive baseline. As the state of the art is a moving target, we inviteresearchers to evaluate their ideas on the live ZeroSCROLLS leaderboard.

ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding