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    <abstract>Existing automatic scientific question generation studies mainly focus on single-document factoid QA, overlooking the inter-document reasoning crucial for scientific understanding. We present AIM-SciQA, an automated framework for generating multi-document, multi-hop scientific QA datasets. AIM-SciQA extracts single-hop QAs using large language models (LLMs) with machine reading comprehension and constructs cross-document relations based on embedding-based semantic alignment while selectively leveraging citation information. Applied to 8,211 PubMed Central papers, it produced 411,409 single-hop and 13,672 multi-hop QAs, forming the IM-SciQA dataset. Human and automatic validation confirmed high factual consistency, and experimental results demonstrate that IM-SciQA effectively differentiates reasoning capabilities across retrieval and QA stages, providing a realistic and interpretable benchmark for retrieval-augmented scientific reasoning. We further extend this framework to construct CIM-SciQA, a citation-guided variant achieving comparable performance to the Oracle setting, reinforcing the dataset’s validity and generality.</abstract>
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%0 Conference Proceedings
%T Automatic Inter-document Multi-hop Scientific QA Generation
%A Lee, Seungmin
%A Kim, Dongha
%A Jeon, Yuni
%A Koh, Junyoung
%A Song, Min
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F lee-etal-2026-automatic
%X Existing automatic scientific question generation studies mainly focus on single-document factoid QA, overlooking the inter-document reasoning crucial for scientific understanding. We present AIM-SciQA, an automated framework for generating multi-document, multi-hop scientific QA datasets. AIM-SciQA extracts single-hop QAs using large language models (LLMs) with machine reading comprehension and constructs cross-document relations based on embedding-based semantic alignment while selectively leveraging citation information. Applied to 8,211 PubMed Central papers, it produced 411,409 single-hop and 13,672 multi-hop QAs, forming the IM-SciQA dataset. Human and automatic validation confirmed high factual consistency, and experimental results demonstrate that IM-SciQA effectively differentiates reasoning capabilities across retrieval and QA stages, providing a realistic and interpretable benchmark for retrieval-augmented scientific reasoning. We further extend this framework to construct CIM-SciQA, a citation-guided variant achieving comparable performance to the Oracle setting, reinforcing the dataset’s validity and generality.
%R 10.63317/3tv9zuz8heht
%U https://aclanthology.org/2026.lrec-1.409/
%U https://doi.org/10.63317/3tv9zuz8heht
%P 5232-5245

Markdown (Informal)

[Automatic Inter-document Multi-hop Scientific QA Generation](https://aclanthology.org/2026.lrec-1.409/) (Lee et al., LREC 2026)

• Automatic Inter-document Multi-hop Scientific QA Generation (Lee et al., LREC 2026)

ACL

• Seungmin Lee, Dongha Kim, Yuni Jeon, Junyoung Koh, and Min Song. 2026. Automatic Inter-document Multi-hop Scientific QA Generation. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 5232–5245, Palma de Mallorca, Spain. ELRA Language Resource Association.