Datasets › FlenQA

FlenQA

Introduced by Mosh Levy et al. in Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models19 Feb 2024 archive 2025-07-28

A synthetically generated QA dataset for text-based reasoning. For each sample, composed of a True/False question over two pieces of information required to answer it (the context), we create multiple versions of different lengths by embedding the context parts within longer, irrelevant texts. To ensure that models utilize their entire input, the dataset is composed of tasks for which both pieces of information must reasoned over together in order to correctly answer the question. At the same time, we keep the tasks simple enough such that models answer most of them correctly when the information pieces are presented on their own, with no additional padding.

Benchmarks archive 2025-07-28

No leaderboard in the archive resolves to this dataset.

Papers archive 2025-07-28

No paper in the archive has a leaderboard row on this dataset; the archive counts 5 papers for it but never published that list.

Dataset loaders archive 2025-07-28

No loader listed in the archive.

Tasks archive 2025-07-28

License archive 2025-07-28

Apache 2.0

Modalities archive 2025-07-28

No modality tagged.

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

  • FlenQA

1 variant name, as the archive lists them.

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