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FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark for Large Language Models (2024.acl-short)

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Challenge: Existing benchmarks for large language models focus on intradocument dependencies or dependencies between a small number of documents.
Approach: They propose to use a dataset of fan-out question-answer pairs and human-annotated decompositions with English Wikipedia as the knowledge base to evaluate models' reasoning.
Outcome: The proposed dataset shows that models still have room to improve reasoning over inter-document dependencies in a long context.

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