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        <title>FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark for Large Language Models</title>
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    <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, along with open-source tools to run models to encourage evaluation.</abstract>
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%0 Conference Proceedings
%T FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark for Large Language Models
%A Zhu, Andrew
%A Hwang, Alyssa
%A Dugan, Liam
%A Callison-Burch, Chris
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F zhu-etal-2024-fanoutqa
%X 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, along with open-source tools to run models to encourage evaluation.
%U https://aclanthology.org/2024.acl-short.2
%P 18-37

Markdown (Informal)

[FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark for Large Language Models](https://aclanthology.org/2024.acl-short.2) (Zhu et al., ACL 2024)

• FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark for Large Language Models (Zhu et al., ACL 2024)

ACL

• Andrew Zhu, Alyssa Hwang, Liam Dugan, and Chris Callison-Burch. 2024. FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark for Large Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 18–37, Bangkok, Thailand. Association for Computational Linguistics.