{"url":"/dataset/fanoutqa","name":"FanOutQA","full_name":null,"description_markdown":"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.","description_withheld":null,"homepage":"https://fanoutqa.com","introduced_date":"2024-02-21","introduced_date_note":null,"introduced_by":{"paper":"/paper/fanoutqa-multi-hop-multi-document-question","title":"FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark for Large Language Models","first_author":"Andrew Zhu","url":null},"license":{"name":"CC BY-SA","url":"https://github.com/zhudotexe/fanoutqa/blob/main/fanoutqa/data/LICENSE"},"modalities":[],"tasks":[{"name":"Question Answering","url":"/task/question-answering","datasets_with_task":"/datasets/task/question-answering"},{"name":"Retrieval","url":"/task/retrieval","datasets_with_task":"/datasets/task/retrieval"}],"languages":[],"variants":["FanOutQA"],"data_loaders":[],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"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."}