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
The rapid progress in LLMs has exposed the limitations of existing benchmarks, necessitating more sophisticated evaluation methods that mirror real-world complexities.
This benchmark addresses a critical gap in AI evaluation, pushing LLMs towards more human-like reasoning over complex, multimodal information, which is essential for advanced AI applications.
The introduction of DocHop-QA shifts the focus of AI development and evaluation from unimodal, short-span reasoning to multi-hop, multimodal, multi-document understanding, raising the bar for LLM capabilities.
- · AI researchers
- · LLM developers
- · Scientific information platforms
- · Healthcare and legal tech
- · LLMs with limited multimodal capabilities
- · Companies relying on outdated QA benchmarks
- · Unimodal data processing methodologies
Improved performance of LLMs in complex reasoning and information synthesis tasks, especially in scientific and professional domains.
Acceleration of AI applications requiring synthesis across diverse data types, leading to new vertical applications in sectors like drug discovery or legal analysis.
Enhanced trust and reliability in AI-driven decision support systems that can transparently reason over distributed evidence.
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Read at arXiv cs.CL