SCROLLS
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.
- Made by
- Tel Aviv University and collaborators
- Released
- January 2022
- Kind
- Reasoning over long documents
- Level
- University
- Scored
- Checked automatically
- Answers
- Open-ended
- Tasks
- Public, plus a private test set
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.
LED tops the leaderboard with 29.2
Best score per entry from the benchmark's own leaderboard, read on November 12th, 2026. Where a model ran with several settings or harnesses, we keep its best. Names in gray aren't in our model list: fine-tunes, agent systems, products and older models. They don't count toward leaders.
| # | Entry | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | LED | 29.2 | 16384 input token limit | Official leaderboard | |
| 2 | BART | 29.0 | 1024 input token limit | Official leaderboard |
No lab model card reports it yet
We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark.
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