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

Paper · 2026 · Mingchao Liu, Yu Sun, Ruixiao Sun et al.

Summary

Content moderation of short videos depends on detailed, policy-specific criteria. General multimodal LLMs grasp overall video meaning but often miss these details when fine-tuned end-to-end on final verdicts only. The TikTok authors propose IPS (In-Prompt Process Supervision), which exploits a by-product of human review: annotators work through a policy decision tree and record intermediate "ancillary" answers. These answers are noisy and usually discarded.

IPS places several ancillary questions in one prompt before the final question, each ending in a special <ans> token. A small MLP head reads the hidden state at each <ans> token to predict that sub-answer, and another head predicts the final label. Training uses a weighted loss, with ancillary terms weighted around 0.1. Everything runs in a single forward pass, so inference cost barely changes.

IPS was evaluated on the public MM-Soc hate speech set (HSD) and two internal tasks, UCC and ANSA. It beat vanilla SFT on F1 and on recall at fixed precision levels. Ancillary labels produced automatically by a general MLLM worked nearly as well as human ones. This makes the approach cheap to scale in industrial moderation.

Key points

  • Tasks and data: IPS was tested on three tasks:
    • HSD (Hate Speech Detection) from MM-Soc, which is public;
    • Unoriginal Content Classification (UCC), which is proprietary;
    • Adult Nudity and Sexual Activity (ANSA), which is proprietary.
  • Robust weighting: Ancillary loss weights from {0, 0.05, 0.1, 0.15, 0.2, 1} were tried. A uniform weight of about 0.1 worked across all three tasks without per-task tuning, when there were fewer than 5 ancillary questions.
  • MLLM-generated labels: On UCC with LLaVA-OV-0.5b, training sets of 12k to 90k samples were compared. IPS using MLLM-generated process labels performed on par with IPS using human labels, and both consistently outperformed vanilla SFT.
  • Quality of zero-shot labels: A manual check of 1,000 UCC samples measured agreement with human labels. Agreement was 57.6% for the final label but 66.95–79.10% for the simpler process questions (roughly 75% overall), so MLLMs are better suited to producing intermediate labels than final ones.
  • Online deployment: ANSA-IPS replaced a legacy X-VLM-based model in a 14-day A/B test on 10% of traffic (p < 0.001). It reduced:
    • inappropriate content view rate (ICVR) by 0.22%;
    • sexual suggestive view rate (SSVR) by 1.3%.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

Cite

Cite as Mingchao Liu, Yu Sun, Ruixiao Sun, Xin Dong, Xiang Shen, Hongwei Wang, Hongyu Xiong and Yang Song. 2026. IPS: In-Prompt Process Supervision for Short Video Content Moderation. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-industry.89

@inproceedings{liu2026ips,
  title = {{IPS: In-Prompt Process Supervision for Short Video Content Moderation}},
  author = {Mingchao Liu, Yu Sun, Ruixiao Sun, Xin Dong, Xiang Shen, Hongwei Wang, Hongyu Xiong and Yang Song},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-industry.89},
  url = {https://doi.org/10.18653/v1/2026.acl-industry.89}
}

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