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
@article{arxiv.2402.14116,
title = {FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark for Large Language Models},
author = {Andrew Zhu and Alyssa Hwang and Liam Dugan and Chris Callison-Burch},
journal= {arXiv preprint arXiv:2402.14116},
year = {2024}
}