MCiteBench: A Multimodal Benchmark for Generating Text with Citations
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 26 references that are in the map. First posted on arXiv 4 Mar 2025.
Builds on
19 of 26 references.
References · 19
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
- 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,151
- 2024Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scalingcited by 1,573
- 2024MiniCPM-V: A GPT-4V Level MLLM on Your Phonecited by 1,061
- 2023Enabling Large Language Models to Generate Text with Citationscited by 584
- 2023Evaluating Verifiability in Generative Search Enginescited by 297
- 2024MinerU: An Open-Source Solution for Precise Document Content Extractioncited by 255
- 2024M3DocRAG: Multi-modal Retrieval is What You Need for Multi-page Multi-document Understandingcited by 110
- 2023Retrieving Multimodal Information for Augmented Generation: A Surveycited by 84
- 2024LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-context QAcited by 78
- 2024Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agentcited by 65
- 2024Attribute First, then Generate: Locally-attributable Grounded Text Generationcited by 54
7 more
- 2024Training Language Models to Generate Text with Citations via Fine-grained Rewardscited by 40
- 2024Related Work and Citation Text Generation: A Surveycited by 17
- 2024ALiiCE: Evaluating Positional Fine-grained Citation Generationcited by 15
- 2024Vision Search Assistant: Empower Vision-Language Models as Multimodal Search Enginescited by 14
- 2024Learning to Generate Answers with Citations via Factual Consistency Modelscited by 13
- 2024Systematic Task Exploration with LLMs: A Study in Citation Text Generationcited by 8
- 2024Contextualizing Generated Citation Textscited by 3
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Citing papers per year: 2025: 1.
1 paper.
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- 2026Purifying Multimodal Retrieval: Fragment-Level Evidence Selection for RAG
- 2024On the Capacity of Citation Generation by Large Language Models
- 2026Multimodal Fact-Level Attribution for Verifiable Reasoning
- 2026MCite-RL: Towards Reliable Multimodal RAG via Citation-enhanced Agentic Reinforcement Learning
- 2025UNIDOC-BENCH: A Unified Benchmark for Document-Centric Multimodal RAG
- 2026Measuring What Matters Beyond Text: Evaluating Multimodal Summaries by Quality, Alignment, and Diversity
- 2024L-CiteEval: Do Long-Context Models Truly Leverage Context for Responding?
- 2024Citekit: A Modular Toolkit for Large Language Model Citation Generation