@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 ({ACL} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/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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<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.</abstract>
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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 (ACL 2026)
%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.
%U https://aclanthology.org/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 (ACL 2026), pages 1277–1288, San Diego, California, USA. Association for Computational Linguistics.