IPS: In-Prompt Process Supervision for Short Video Content Moderation

Mingchao Liu, Yu Sun, Ruixiao Sun, Xin Dong, Xiang Shen, Hongwei Wang, Hongyu Xiong, Yang Song

Keywords: Video Recommendation, Content Understanding, LLM, Process Supervision, Noise-aware Learning, LoRA, Multi Modality

Abstract:

Multimodal large language models (MLLMs) are effective at capturing the semantics of short video content; however, they often fail to attend to the policy-specific details required for reliable content moderation.
To address this limitation, we introduce IPS, a novel framework that integrates In-prompt Process Supervision into MLLMs by introducing sequential reasoning over ancillary questions during fine-tuning.
IPS consistently outperforms baseline MLLMs on public and proprietary benchmarks.
Moreover, replacing human-annotated ancillary labels with MLLM-generated ones results in only marginal performance degradation, demonstrating robustness to noisy supervision and strong scalability with model-generated annotations.
These findings establish IPS as a scalable and effective solution for complex multimodal classification in large-scale industrial settings.

Submission Type: Deployed

Submission Number: 302