@inproceedings{wang-etal-2025-reasoning-enhanced,
    title = "Reasoning-Enhanced Domain-Adaptive Pretraining of Multimodal Large Language Models for Short Video Content Governance",
    author = "Wang, Zixuan  and
      Sun, Yu  and
      Wang, Hongwei  and
      Jing, Baoyu  and
      Shen, Xiang  and
      Dong, Xin  and
      Hao, Zhuolin  and
      Xiong, Hongyu  and
      Song, Yang",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.77/",
    pages = "1104--1112",
    ISBN = "979-8-89176-333-3",
    abstract = "Short video platforms are evolving rapidly, making the identification of inappropriate content increasingly critical.Existing approaches typically train separate and small classification models for each type of issue, which requires extensive human-labeled data and lacks cross-issue generalization.We propose a reasoning-enhanced multimodal large language model (MLLM) pretraining paradigm for unified inappropriate content detection. To address the distribution gap between short video content and the original pretraining data of MLLMs, as well as the complex issue definitions, we introduce three targeted pretraining tasks:(1) \textit{Caption}, to enhance the MLLM{'}s perception of video details;(2) \textit{Visual Question Answering (VQA)}, to deepen the MLLM{'}s understanding of issue definitions and annotation guidelines;(3) \textit{Chain-of-Thought (CoT)}, to enhance the MLLM{'}s reasoning capability.Experimental results show that our pretraining approach significantly improves the MLLM{'}s performance in both zero-shot and supervised fine-tuning (SFT) settings.In addition, our pretrained model demonstrates strong generalization capabilities to emergent, previously unseen issues."
}