# SCROLLS: Standardized CompaRison Over Long Language Sequences Source: [SCROLLS: Standardized CompaRison Over Long Language Sequences]() Collection index: [llms.txt]() Data updated: 2026-01-01T15:14:50.558Z Authors: Uri Shaham, Elad Segal, Maor Ivgi, Avia Efrat, Ori Yoran, Adi Haviv, Ankit Gupta, Wenhan Xiong, Mor Geva, Jonathan Berant, Omer Levy Published: 2022-01-10T18:47:15.000Z ## Abstract NLP benchmarks have largely focused on short texts, such as sentences and paragraphs, even though long texts comprise a considerable amount of natural language in the wild. We introduce SCROLLS, a suite of tasks that require reasoning over long texts. We examine existing long-text datasets, and handpick ones where the text is naturally long, while prioritizing tasks that involve synthesizing information across the input. SCROLLS contains summarization, question answering, and natural language inference tasks, covering multiple domains, including literature, science, business, and entertainment. Initial baselines, including Longformer Encoder-Decoder, indicate that there is ample room for improvement on SCROLLS. We make all datasets available in a unified text-to-text format and host a live leaderboard to facilitate research on model architecture and pretraining methods. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Long-context language understanding on SCROLLS (test)](): Long-context Language Understanding; SCROLLS (test); Average Score: 29.2. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()