DocHop-QA: Towards Multi-Hop Reasoning over Multimodal Document Collections

arXiv:2508.15851v2 Announce Type: replace 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 QA benchmarks 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 the long-context and multi-evidence demands of DocHop-QA, establishing it as a rigorous testbed for advancing next-generation scientific QA systems.

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 QA benchmarks 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 the long-context and multi-evidence demands of DocHop-QA, establishing it as a rigorous testbed for advancing next-generation scientific QA systems.

Computation and Language (cs.CL)

arXiv:2508.15851 [cs.CL]

(or arXiv:2508.15851v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2508.15851 Focus to learn more arXiv-issued DOI via DataCite

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DocHop-QA: Towards Multi-Hop Reasoning over Multimodal Document Collections

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