Pith. sign in

REVIEW 10 cited by

SCROLLS: Standardized CompaRison Over Long Language Sequences

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2201.03533 v2 pith:DAXVHETO submitted 2022-01-10 cs.CL cs.AIcs.LGstat.ML

SCROLLS: Standardized CompaRison Over Long Language Sequences

classification cs.CL cs.AIcs.LGstat.ML
keywords longscrollslanguagetaskstextsdatasetsincludingnatural
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original 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.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Positional Failures in Long-Context LLMs: A Blind Spot in Reasoning Benchmarks

    cs.CL 2026-05 conditional novelty 7.0

    Audits reveal no reasoning benchmark controls position/filler/length jointly; CRE shows LLMs drop up to 88pp on middle-position tasks at 64K context, with diagnostic probe supporting positional cause.

  2. FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision

    cs.LG 2024-07 accept novelty 7.0

    FlashAttention-3 achieves 1.5-2x speedup on H100 GPUs for attention, reaching 740 TFLOPs/s (75% utilization) in FP16 and near 1.2 PFLOPs/s in FP8 while cutting numerical error by 2.6x versus baseline FP8 attention.

  3. Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads

    cs.LG 2024-01 conditional novelty 7.0

    Medusa augments LLMs with multiple decoding heads and tree-based attention to predict and verify several tokens in parallel, yielding 2.2-3.6x inference speedup via two fine-tuning regimes.

  4. HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs

    cs.CL 2026-07 conditional novelty 6.0

    HSS-Synth generates 230k instruction-tuning samples for 14 humanities/social-science fields and reports state-of-the-art fine-tuning results on 16 benchmarks.

  5. Hierarchical Grading in Large Language Models

    cs.LG 2026-07 conditional novelty 6.0

    Grading coordinates by the mismatch between target energy and corpus variance lowers sample complexity to the squared Bhattacharyya affinity of the two profiles.

  6. An Empirical Study of Mamba-based Language Models

    cs.LG 2024-06 accept novelty 6.0

    An 8B Mamba-2-Hybrid with 43% Mamba-2, 7% attention, and 50% MLP layers exceeds an 8B Transformer by 2.65 points on average across 12 tasks and matches it on 23 long-context tasks while enabling up to 8x faster inference.

  7. Retentive Network: A Successor to Transformer for Large Language Models

    cs.CL 2023-07 unverdicted novelty 6.0

    RetNet is a new sequence modeling architecture that delivers parallel training, constant-time inference, and competitive language modeling performance as a potential replacement for Transformers.

  8. Kaczmarz Linear Attention

    cs.LG 2026-05 unverdicted novelty 5.0

    Kaczmarz Linear Attention replaces the empirical coefficient in Gated DeltaNet with a key-norm-normalized step size derived from the online regression objective, yielding lower perplexity and better needle-in-haystack...

  9. Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning

    eess.SY 2026-04 unverdicted novelty 5.0

    High-resolution power profiles for AI workloads on H100 GPUs are measured and scaled to whole-facility energy demand using a bottom-up model, with the dataset made public.

  10. Memory for Large Language Models

    cs.CL 2026-07 conditional novelty 4.0

    The paper organizes LLM memory architectures along three axes—implicit vs explicit representation, offline vs online update, and short vs long-term persistence—plus finer-grained update rules.