{"url":"/dataset/lra","name":"LRA","full_name":"Long-Range Arena","description_markdown":"Long-range arena (LRA) is an effort toward systematic evaluation of efficient transformer models. The project aims at establishing benchmark tasks/datasets using which we can evaluate transformer-based models in a systematic way, by assessing their generalization power, computational efficiency, memory foot-print, etc. Long-Range Arena is specifically focused on evaluating model quality under long-context scenarios. The benchmark is a suite of tasks consisting of sequences ranging from 1K to 16K tokens, encompassing a wide range of data types and modalities such as text, natural, synthetic images, and mathematical expressions requiring similarity, structural, and visual-spatial reasoning.\r\n\r\nDescription from: [Long Range Arena : A Benchmark for Efficient Transformers](https://arxiv.org/pdf/2011.04006v1.pdf)","description_withheld":null,"homepage":"https://github.com/google-research/long-range-arena","introduced_date":"2020-11-08","introduced_date_note":null,"introduced_by":{"paper":"/paper/long-range-arena-a-benchmark-for-efficient-1","title":"Long Range Arena: A Benchmark for Efficient Transformers","first_author":"Yi Tay","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Language Modelling","url":"/task/language-modelling","datasets_with_task":"/datasets/task/language-modelling"},{"name":"Long-range modeling","url":"/task/long-range-modeling","datasets_with_task":"/datasets/task/long-range-modeling"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["LRA"],"data_loaders":[{"repo":"https://github.com/google-research/long-range-arena","url":"https://github.com/google-research/long-range-arena","frameworks":[]}],"num_papers_in_archive":180,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/long-range-modeling-on-lra","task":"Long-range modeling","dataset_variant":"LRA","rows":7,"metrics":["Avg","Image","ListOps","Pathfinder","Text","Retrieval","Pathfinder-X"],"first_row_in_archive_order":{"model":"S5","paper":"/paper/simplified-state-space-layers-for-sequence","metrics":{"Avg":"87.46","Image":"88","ListOps":"62.15","Pathfinder":"95.33","Pathfinder-X":"98.58","Retrieval":"91.4","Text":"89.31"},"code_links":[{"title":"lindermanlab/S5","url":"https://github.com/lindermanlab/S5"},{"title":"uzh-rpg/ssms_event_cameras","url":"https://github.com/uzh-rpg/ssms_event_cameras"},{"title":"Efficient-Scalable-Machine-Learning/event-ssm","url":"https://github.com/Efficient-Scalable-Machine-Learning/event-ssm"},{"title":"msgwak/last","url":"https://github.com/msgwak/last"},{"title":"leonty1/essm","url":"https://github.com/leonty1/essm"},{"title":"leonty1/deepldnn","url":"https://github.com/leonty1/deepldnn"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/converting-transformers-into-dgnns-form-1","title":"Converting Transformers into DGNNs Form","date":"2025-02-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/simplified-state-space-layers-for-sequence","title":"Simplified State Space Layers for Sequence Modeling","date":"2022-08-09","rows_on_this_dataset":1,"code_links":6,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":19,"samples_ran":12,"samples_unverified":7,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/how-to-train-your-hippo-state-space-models","title":"How to Train Your HiPPO: State Space Models with Generalized Orthogonal Basis Projections","date":"2022-06-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/long-range-arena-a-benchmark-for-efficient-1","title":"Long Range Arena: A Benchmark for Efficient Transformers","date":"2020-11-08","rows_on_this_dataset":4,"code_links":5,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":3,"samples_ran":2,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":2,"samples_harvested":22,"samples_ran":14,"samples_unverified":8,"pointer_only_for_licence":3,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}