Papers › CoLT5: Faster Long-Range Transformers with Conditional Computation

CoLT5: Faster Long-Range Transformers with Conditional Computation

17 Mar 2023arXiv:2303.09752archive 2025-07-28

Joshua Ainslie, Tao Lei, Michiel de Jong, Santiago Ontañón, Siddhartha Brahma, Yury Zemlyanskiy, David Uthus, Mandy Guo, James Lee-Thorp, Yi Tay, Yun-Hsuan Sung, Sumit Sanghai

Many natural language processing tasks benefit from long inputs, but processing long documents with Transformers is expensive -- not only due to quadratic attention complexity but also from applying feedforward and projection layers to every token. However, not all tokens are equally important, especially for longer documents. We propose CoLT5, a long-input Transformer model that builds on this intuition by employing conditional computation, devoting more resources to important tokens in both feedforward and attention layers. We show that CoLT5 achieves stronger performance than LongT5 with much faster training and inference, achieving SOTA on the long-input SCROLLS benchmark. Moreover, CoLT5 can effectively and tractably make use of extremely long inputs, showing strong gains up to 64k input length.

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Tasks

Long-range modeling

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Long-range modeling SCROLLS CoLT5 XL Avg. 43.51 #1 of 13 Archive leaderboard report
Long-range modeling SCROLLS CoLT5 XL CNLI 88.4 #1 of 13 Archive leaderboard report
Long-range modeling SCROLLS CoLT5 XL GovRep 61.3/32.2/33.8 #1 of 13 Archive leaderboard report
Long-range modeling SCROLLS CoLT5 XL Nrtv 31.1 #1 of 13 Archive leaderboard report
Long-range modeling SCROLLS CoLT5 XL QALT EM-T/H 48.1/43.8 #1 of 13 Archive leaderboard report
Long-range modeling SCROLLS CoLT5 XL QMSum 36.2/12.9/24.3 #1 of 13 Archive leaderboard report
Long-range modeling SCROLLS CoLT5 XL Qspr 53.9 #1 of 13 Archive leaderboard report
Long-range modeling SCROLLS CoLT5 XL SumScr 36.4/10.2/21.7 #1 of 13 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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