@inproceedings{liu-etal-2026-ips,
title = "{IPS}: In-Prompt Process Supervision for Short Video Content Moderation",
author = "Liu, Mingchao and
Sun, Yu and
Sun, Ruixiao and
Dong, Xin and
Shen, Xiang and
Wang, Hongwei and
Xiong, Hongyu and
Song, Yang",
editor = "Li, Yunyao and
Rehm, Georg and
Tu, Mei",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 6: Industry Track)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-industry.89/",
doi = "10.18653/v1/2026.acl-industry.89",
pages = "1277--1288",
ISBN = "979-8-89176-394-4",
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."
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%0 Conference Proceedings
%T IPS: In-Prompt Process Supervision for Short Video Content Moderation
%A Liu, Mingchao
%A Sun, Yu
%A Sun, Ruixiao
%A Dong, Xin
%A Shen, Xiang
%A Wang, Hongwei
%A Xiong, Hongyu
%A Song, Yang
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F liu-etal-2026-ips
%X 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.
%R 10.18653/v1/2026.acl-industry.89
%U https://aclanthology.org/2026.acl-industry.89/
%U https://doi.org/10.18653/v1/2026.acl-industry.89
%P 1277-1288
Markdown (Informal)
[IPS: In-Prompt Process Supervision for Short Video Content Moderation](https://aclanthology.org/2026.acl-industry.89/) (Liu et al., ACL 2026)
ACL
- 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. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track), pages 1277–1288, San Diego, California, USA. Association for Computational Linguistics.