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arxiv: 2412.15251 · v3 · submitted 2024-12-15 · 💻 cs.CL · cs.AI

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 This is my paper

Pith reviewed 2026-05-23 06:59 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords content moderationmultimodal large language modelsprocess supervisionshort videofine-tuningancillary questionsnoisy supervision
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The pith

In-prompt process supervision improves MLLM performance on short video moderation by directing attention to policy-specific details through sequential reasoning over ancillary questions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces IPS as a fine-tuning method that embeds sequential reasoning over ancillary questions directly into the prompt for multimodal large language models. This targets the gap where standard MLLMs capture overall video semantics but overlook the precise policy rules needed for reliable moderation decisions. The authors report consistent gains over baseline models on both public and internal benchmarks, and they show that the same gains appear when the ancillary labels come from other MLLMs rather than humans. The result matters for platforms that must moderate large volumes of short video at low cost, because the approach reduces dependence on fresh human annotations while preserving accuracy.

Core claim

IPS integrates in-prompt process supervision by requiring the model, during fine-tuning, to answer a sequence of ancillary questions that surface policy-relevant details before producing the final moderation label. This structured reasoning path improves accuracy on policy-specific classification tasks compared with ordinary supervised fine-tuning of the same base MLLMs. The performance advantage remains largely intact when the ancillary labels are generated by another MLLM instead of human annotators.

What carries the argument

In-prompt Process Supervision (IPS), a fine-tuning procedure that inserts sequential reasoning over a fixed set of ancillary policy questions into the training prompt.

If this is right

  • IPS raises moderation accuracy on both public and proprietary short-video benchmarks relative to standard MLLM fine-tuning.
  • Replacing human ancillary labels with labels produced by another MLLM produces only small drops in final performance.
  • The method remains effective under noisy supervision, supporting use at industrial scale where fresh human labels are expensive.
  • The same training pattern can be applied to other complex multimodal classification problems that require attention to rule-based details.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach could be tested on tasks outside moderation, such as medical image reporting or legal document classification, where policy-like rules must be followed precisely.
  • If the ancillary questions can be generated automatically from policy text, the entire pipeline could run with almost no human labeling after the initial question design.
  • A natural next measurement would be whether the same sequential prompting improves zero-shot or few-shot performance on new policies without additional fine-tuning.

Load-bearing premise

That inserting a fixed sequence of ancillary questions will consistently draw the model's attention to the exact policy details it otherwise ignores, rather than simply adding irrelevant steps or causing overfitting to the question set.

What would settle it

An experiment in which MLLMs fine-tuned with IPS show no accuracy gain, or a loss, on the same policy-specific moderation test cases relative to identical models fine-tuned without the ancillary-question sequence.

Figures

Figures reproduced from arXiv: 2412.15251 by Hongwei Wang, Hongyu Xiong, Mingchao Liu, Ruixiao Sun, Xiang Shen, Xin Dong, Yang Song, Yu Sun.

Figure 1
Figure 1. Figure 1: AgentPS Framework. It comprises three key components: a vision encoder for handling images, a vision-language modality alignment projector, and a language model that integrates visual and textual tokens. The framework integrates N + 1 questions into one input prompts during the SFT process for efficiency in training and future deployment. Each ancillary question concludes with an <ans> token, whose hidden … view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The manuscript introduces IPS, a framework for In-Prompt Process Supervision in MLLMs for short video content moderation. It incorporates sequential reasoning over ancillary questions during fine-tuning, claiming consistent outperformance versus baseline MLLMs on public and proprietary benchmarks plus robustness when human-annotated ancillary labels are replaced by MLLM-generated ones.

Significance. If the empirical claims hold with substantial, reproducible gains, IPS could supply a scalable route to policy-specific multimodal classification that reduces dependence on human annotation while preserving accuracy in industrial moderation pipelines.

major comments (1)
  1. [Abstract] Abstract: the central claims assert that IPS 'consistently outperforms baseline MLLMs' and exhibits 'only marginal performance degradation' under MLLM-generated labels, yet the abstract (and the supplied material) contains no quantitative results, benchmark names, ablation studies, or error analysis. These assertions therefore remain unsupported, which is load-bearing for any evaluation of the contribution.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the single major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claims assert that IPS 'consistently outperforms baseline MLLMs' and exhibits 'only marginal performance degradation' under MLLM-generated labels, yet the abstract (and the supplied material) contains no quantitative results, benchmark names, ablation studies, or error analysis. These assertions therefore remain unsupported, which is load-bearing for any evaluation of the contribution.

    Authors: We agree that the abstract would be strengthened by including concrete quantitative highlights to make the central claims immediately evaluable. The full manuscript already reports benchmark names, ablation studies, and error analyses in the Experiments and Analysis sections. In the revised version we will update the abstract to include specific performance deltas (e.g., accuracy improvements on the public and proprietary benchmarks) and the observed degradation range when switching to model-generated ancillary labels. This change directly addresses the concern while preserving the abstract's brevity. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The supplied abstract and description contain no equations, derivations, fitted parameters, or self-citation chains. The central claims are framed as empirical benchmark comparisons (outperformance of baselines and robustness to MLLM-generated labels) rather than quantities defined in terms of the method's own inputs. No load-bearing step reduces by construction to a fit or prior self-citation; the work is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Only the abstract is available, so the ledger is necessarily incomplete. The central claim rests on the unstated premise that ancillary questions can be chosen or generated in a way that captures policy details without introducing new failure modes.

axioms (1)
  • domain assumption Ancillary questions exist that, when answered sequentially, surface policy-specific details missed by standard MLLM attention.
    This premise is required for the in-prompt supervision step to improve moderation accuracy; it is invoked implicitly in the description of IPS.

pith-pipeline@v0.9.0 · 5661 in / 1253 out tokens · 40960 ms · 2026-05-23T06:59:54.611911+00:00 · methodology

discussion (0)

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Reference graph

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