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    <abstract>Despite rapid progress in large language models (LLMs), current QA benchmarks still overlook the core challenge of real-world scientific information seeking: synthesizing multimodal evidence scattered across multiple documents and structural formats. Existing QAs remain narrow in scope, relying on unimodal text and short-span reasoning that fail to capture the complexity of real information-seeking. We introduce DocHop-QA, a benchmark of 11,379 instances for evaluating multimodal, multi-document, multi-hop scientific QA. Built from publicly available PubMed articles, DocHop-QA incorporates textual passages, tables, and layout cues, enabling cross-document inference without explicit hyperlinks. To scale realistic QA construction, we develop an LLM-driven generation pipeline grounded in 11 scientific reasoning concepts, producing diverse and coherent question–answer pairs. To highlight the utility and versatility of the dataset, we propose a task-driven evaluation framework spanning four settings, including generative answering, multimodal evidence integration and structured index prediction. Experiments show that current models struggle with DocHop-QA’s long-context, multi-evidence demands, establishing it as a rigorous testbed for advancing next-generation scientific QA systems.</abstract>
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%0 Conference Proceedings
%T DocHop-QA: Towards Multi-Hop Reasoning over Multimodal Document Collections
%A Park, Jiwon
%A Pyeon, Seohyun
%A Kim, Jinwoo
%A Cabral, Rina Carines
%A He, Zhenyuan
%A Ding, Yihao
%A Han, Caren
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F park-etal-2026-dochop
%X Despite rapid progress in large language models (LLMs), current QA benchmarks still overlook the core challenge of real-world scientific information seeking: synthesizing multimodal evidence scattered across multiple documents and structural formats. Existing QAs remain narrow in scope, relying on unimodal text and short-span reasoning that fail to capture the complexity of real information-seeking. We introduce DocHop-QA, a benchmark of 11,379 instances for evaluating multimodal, multi-document, multi-hop scientific QA. Built from publicly available PubMed articles, DocHop-QA incorporates textual passages, tables, and layout cues, enabling cross-document inference without explicit hyperlinks. To scale realistic QA construction, we develop an LLM-driven generation pipeline grounded in 11 scientific reasoning concepts, producing diverse and coherent question–answer pairs. To highlight the utility and versatility of the dataset, we propose a task-driven evaluation framework spanning four settings, including generative answering, multimodal evidence integration and structured index prediction. Experiments show that current models struggle with DocHop-QA’s long-context, multi-evidence demands, establishing it as a rigorous testbed for advancing next-generation scientific QA systems.
%U https://aclanthology.org/2026.emnlp-main.2563/
%P 58233-58261

Markdown (Informal)

[DocHop-QA: Towards Multi-Hop Reasoning over Multimodal Document Collections](https://aclanthology.org/2026.emnlp-main.2563/) (Park et al., EMNLP 2026)

• DocHop-QA: Towards Multi-Hop Reasoning over Multimodal Document Collections (Park et al., EMNLP 2026)

ACL

• Jiwon Park, Seohyun Pyeon, Jinwoo Kim, Rina Carines Cabral, Zhenyuan He, Yihao Ding, and Caren Han. 2026. DocHop-QA: Towards Multi-Hop Reasoning over Multimodal Document Collections. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 58233–58261, Budapest, Hungary. Association for Computational Linguistics.