Datasets › FanOutQA

FanOutQA

Introduced by Andrew Zhu et al. in FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark for Large Language Models21 Feb 2024 archive 2025-07-28

FanOutQA is a high quality, multi-hop, multi-document benchmark for large language models using English Wikipedia as its knowledge base. Compared to other question-answering benchmarks, FanOutQA requires reasoning over a greater number of documents, with the benchmark's main focus being on the titular fan-out style of question. We present these questions in three tasks -- closed-book, open-book, and evidence-provided -- which measure different abilities of LLM systems.

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 3 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

CC BY-SA

Modalities archive 2025-07-28

No modality tagged.

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

  • FanOutQA

1 variant name, as the archive lists them.

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