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This arXiv paper proposes a staged framework for automating short-video content labeling, moving from a fine-tuned multimodal LLM classifier (L1) to zero-shot labeling with a domain-adapted model (L2) to an agentic workflow with tools (L3). The headline claim is up to 85% reduction in manual labeling costs, but that figure comes from the authors' real-world deployment and the abstract gives no benchmarks or baselines to check it against. Readers interested in agentic systems for moderation or annotation should look at the experimental setup before treating the cost savings as transferable.
Towards Autopilot Labeling for Short Video Content Governance
arXiv:2610.15687v1 Announce Type: new Abstract: The explosive growth of short videos has presented significant challenges to content governance. Traditional manual labeling is inefficient, slow to iterate, and costly. Therefore, we explore and develop a multi-stage autopilot labeling framework aimed at progressively increasing the level of automation in short video content annotation. To move beyond the heavy reliance on human expert labeling at the initial L0 stage, we first introduce a discriminative MLLM labeler at the L1 stage, which is fine-tuned with supervised data to directly predict labels for short videos. Advancing to the L2 stage, we shift to a domain-adaptive MLLM pretrained on short video data and use it for zero-shot labeling, enabling flexible generalization without task-specific supervision. In the L3 stage, we propose an agentic annotation workflow, and equip the agent with several key tools, which empower the agent to handle complex, unfamiliar, or vaguely defined cases, significantly reducing the need for human intervention. We conduct extensive experiments across various content issues, and results demonstrate that each stage of the autopilot labeling framework progressively improves labeling accuracy, and has significantly reduced manual labor costs in real-world applications by up to 85%.
Abstract:The explosive growth of short videos has presented significant challenges to content governance. Traditional manual labeling is inefficient, slow to iterate, and costly. Therefore, we explore and develop a multi-stage autopilot labeling framework aimed at progressively increasing the level of automation in short video content annotation. To move beyond the heavy reliance on human expert labeling at the initial L0 stage, we first introduce a discriminative MLLM labeler at the L1 stage, which is fine-tuned with supervised data to directly predict labels for short videos. Advancing to the L2 stage, we shift to a domain-adaptive MLLM pretrained on short video data and use it for zero-shot labeling, enabling flexible generalization without task-specific supervision. In the L3 stage, we propose an agentic annotation workflow, and equip the agent with several key tools, which empower the agent to handle complex, unfamiliar, or vaguely defined cases, significantly reducing the need for human intervention. We conduct extensive experiments across various content issues, and results demonstrate that each stage of the autopilot labeling framework progressively improves labeling accuracy, and has significantly reduced manual labor costs in real-world applications by up to 85%.
15 pages. Accepted to EMNLP 2026
Computation and Language (cs.CL)
arXiv:2610.15687 [cs.CL]
(or arXiv:2610.15687v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.15687 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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