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    <abstract>Multimodal Large Language Models (MLLMs) have advanced in integrating diverse modalities but frequently suffer from hallucination. A promising solution to mitigate this issue is to generate text with citations, providing a transparent chain for verification. However, existing work primarily focuses on generating citations for text-only content, leaving the challenges of multimodal scenarios largely unexplored. In this paper, we introduce MCiteBench, the first benchmark designed to assess the ability of MLLMs to generate text with citations in multimodal contexts. Our benchmark comprises data derived from academic papers and review-rebuttal interactions, featuring diverse information sources and multimodal content. Experimental results reveal that MLLMs struggle to ground their outputs reliably when handling multimodal input. Further analysis uncovers a systematic modality bias and reveals how models internally rely on different sources when generating citations, offering insights into model behavior and guiding future directions for multimodal citation tasks.</abstract>
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%0 Conference Proceedings
%T MCiteBench: A Multimodal Benchmark for Generating Text with Citations
%A Hu, Caiyu
%A Zhang, Yikai
%A Zhu, Tinghui
%A Ye, Yiwei
%A Xiao, Yanghua
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F hu-etal-2025-mcitebench
%X Multimodal Large Language Models (MLLMs) have advanced in integrating diverse modalities but frequently suffer from hallucination. A promising solution to mitigate this issue is to generate text with citations, providing a transparent chain for verification. However, existing work primarily focuses on generating citations for text-only content, leaving the challenges of multimodal scenarios largely unexplored. In this paper, we introduce MCiteBench, the first benchmark designed to assess the ability of MLLMs to generate text with citations in multimodal contexts. Our benchmark comprises data derived from academic papers and review-rebuttal interactions, featuring diverse information sources and multimodal content. Experimental results reveal that MLLMs struggle to ground their outputs reliably when handling multimodal input. Further analysis uncovers a systematic modality bias and reveals how models internally rely on different sources when generating citations, offering insights into model behavior and guiding future directions for multimodal citation tasks.
%U https://aclanthology.org/2025.findings-emnlp.318/
%P 5949-5966

Markdown (Informal)

[MCiteBench: A Multimodal Benchmark for Generating Text with Citations](https://aclanthology.org/2025.findings-emnlp.318/) (Hu et al., Findings 2025)

• MCiteBench: A Multimodal Benchmark for Generating Text with Citations (Hu et al., Findings 2025)

ACL

• Caiyu Hu, Yikai Zhang, Tinghui Zhu, Yiwei Ye, and Yanghua Xiao. 2025. MCiteBench: A Multimodal Benchmark for Generating Text with Citations. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 5949–5966, Suzhou, China. Association for Computational Linguistics.