# FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark for Large Language Models

Source: [FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark for Large Language Models](<https://www.sota2.com/research/paper/68fa738b1e209131cea5d1b6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-02T06:19:40.898Z

Authors: Andrew Zhu, Alyssa Hwang, Liam Dugan, Chris Callison-Burch

Published: 2024-02-21T20:30:45.000Z

## Abstract

One type of question that is commonly found in day-to-day scenarios is \`\`fan-out'' questions, complex multi-hop, multi-document reasoning questions that require finding information about a large number of entities. However, there exist few resources to evaluate this type of question-answering capability among large language models. To evaluate complex reasoning in LLMs more fully, we present FanOutQA, a high-quality dataset of fan-out question-answer pairs and human-annotated decompositions with English Wikipedia as the knowledge base. We formulate three benchmark settings across our dataset and benchmark 7 LLMs, including GPT-4, LLaMA 2, Claude-2.1, and Mixtral-8x7B, finding that contemporary models still have room to improve reasoning over inter-document dependencies in a long context. We provide our dataset and open-source tools to run models to encourage evaluation at https://fanoutqa.com

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2402.14116>)

[PDF](<https://arxiv.org/pdf/2402.14116.pdf>)

[Implementation](<https://github.com/zhudotexe/fanoutqa>)