arXiv:2606.27187v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) have recently shown immense potential in automated content moderation, sparking growing interest in developing harmful-video benchmarks. However, we identify two primary limitations in existing works: 1) The multi-layered characteristics of harmful videos are overlooked. Existing benchmarks predominantly formulate evaluation as a binary classification task, failing to capture implicit or deep contextual harms. 2) Explanatory rationales are completely absent. Current frameworks measure exclusively whether a m
The rapid advancement and widespread deployment of Large Multimodal Models necessitate more robust and nuanced approaches to content moderation, especially for complex and harmful video content.
Improved harmful video benchmarking directly impacts the safety and ethical development of AI, influencing regulatory frameworks, platform policies, and public trust in AI moderation capabilities.
The focus for harmful content moderation shifts towards multi-layered analyses and explanatory rationales, moving beyond simple binary classifications, leading to more sophisticated and transparent AI systems.
- · AI safety researchers
- · Social media platforms
- · Content moderation tech providers
- · Platforms with weak content moderation
- · Developers of simplistic AI moderation tools
New benchmarks push the development of more sophisticated and ethically aligned Large Multimodal Models (LVLMs) for content moderation.
Enhanced moderation capabilities lead to reduced harmful content exposure, improving user experience and potentially mitigating social harms.
The demand for explainable AI in moderation could drive broader adoption of interpretive AI features across various applications, fostering greater transparency and accountability.
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