Benchmark Almanac

SCROLLS

Not in model cards

Models do seven long-text tasks such as summarization, question answering and natural language inference over naturally long documents.

ZeroSCROLLS, a zero-shot follow-up, is a separate test.

In the authors’ words

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.

Abstract of SCROLLS: Standardized CompaRison Over Long Language Sequences (arXiv:2201.03533, CC0)
Made by Tel Aviv University and collaborators Released Jan 2022 Reasoning over long documents Public tasks plus a private test set
Level
University Undergraduates and entry-level professionals
Scored
Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
Answers
Open-ended Free-form text, documents, designs or media judged as a whole.

No scores here yet

We have no scores for SCROLLS yet.

See the live leaderboard on scrolls-benchmark.com

No lab model card reports it yet

We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark.

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