Understanding and Benchmarking the Trustworthiness in Multimodal LLMs for Video Understanding

• URL: http://arxiv.org/abs/2506.12336v1
• Date: Sat, 14 Jun 2025 04:04:54 GMT
• Title: Understanding and Benchmarking the Trustworthiness in Multimodal LLMs for Video Understanding
• Authors: Youze Wang, Zijun Chen, Ruoyu Chen, Shishen Gu, Yinpeng Dong, Hang Su, Jun Zhu, Meng Wang, Richang Hong, Wenbo Hu,
• Abstract summary: This study introduces a benchmark evaluating videoLLMs across five dimensions: truthfulness, safety, fairness, and privacy.<n>Our evaluation of 23 state-of-the-art videoLLMs reveals significant limitations in dynamic visual scene understanding and cross-modal resilience.
• Score: 59.75428247670665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in multimodal large language models for video understanding (videoLLMs) have improved their ability to process dynamic multimodal data. However, trustworthiness challenges factual inaccuracies, harmful content, biases, hallucinations, and privacy risks, undermine reliability due to video data's spatiotemporal complexities. This study introduces Trust-videoLLMs, a comprehensive benchmark evaluating videoLLMs across five dimensions: truthfulness, safety, robustness, fairness, and privacy. Comprising 30 tasks with adapted, synthetic, and annotated videos, the framework assesses dynamic visual scenarios, cross-modal interactions, and real-world safety concerns. Our evaluation of 23 state-of-the-art videoLLMs (5 commercial,18 open-source) reveals significant limitations in dynamic visual scene understanding and cross-modal perturbation resilience. Open-source videoLLMs show occasional truthfulness advantages but inferior overall credibility compared to commercial models, with data diversity outperforming scale effects. These findings highlight the need for advanced safety alignment to enhance capabilities. Trust-videoLLMs provides a publicly available, extensible toolbox for standardized trustworthiness assessments, bridging the gap between accuracy-focused benchmarks and critical demands for robustness, safety, fairness, and privacy.

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