Detecting Harmful Memes with Decoupled Understanding and Guided CoT
  Reasoning
Detecting harmful memes is essential for maintaining the integrity of online
environments. However, current approaches often struggle with resource
efficiency, flexibility, or explainability, limiting their practical deployment
in content moderation systems. To address these challenges, we introduce
U-CoT+, a novel framework for harmful meme detection. Instead of relying solely
on prompting or fine-tuning multimodal models, we first develop a high-fidelity
meme-to-text pipeline that converts visual memes into detail-preserving textual
descriptions. This design decouples meme interpretation from meme
classification, thus avoiding immediate reasoning over complex raw visual
content and enabling resource-efficient harmful meme detection with general
large language models (LLMs). Building on these textual descriptions, we
further incorporate targeted, interpretable human-crafted guidelines to guide
models' reasoning under zero-shot CoT prompting. As such, this framework allows
for easy adaptation to different harmfulness detection criteria across
platforms, regions, and over time, offering high flexibility and
explainability. Extensive experiments on seven benchmark datasets validate the
effectiveness of our framework, highlighting its potential for explainable and
low-resource harmful meme detection using small-scale LLMs. Codes and data are
available at: https://anonymous.4open.science/r/HMC-AF2B/README.md.
684fb8cb60b4a34dbe007b05
Fengjun Pan
684fb8cb60b4a34dbe007b06
Anh Tuan Luu
684fb8cb60b4a34dbe007b07
Xiaobao Wu
claimed_verified
2025-06-16T07:16:04.938Z
64cb02869e30a46f7b80b355
/avatars/81ce4ba78826b54f0e1b53eeaff87ee6.svg
Xiaobao Wu
user
bobxwu
Detecting Harmful Memes with Decoupled Understanding and Guided CoT
  Reasoning
Detecting harmful memes is essential for maintaining the integrity of online
environments. However, current approaches often struggle with resource
efficiency, flexibility, or explainability, limiting their practical deployment
in content moderation systems. To address these challenges, we introduce
U-CoT+, a novel framework for harmful meme detection. Instead of relying solely
on prompting or fine-tuning multimodal models, we first develop a high-fidelity
meme-to-text pipeline that converts visual memes into detail-preserving textual
descriptions. This design decouples meme interpretation from meme
classification, thus avoiding immediate reasoning over complex raw visual
content and enabling resource-efficient harmful meme detection with general
large language models (LLMs). Building on these textual descriptions, we
further incorporate targeted, interpretable human-crafted guidelines to guide
models' reasoning under zero-shot CoT prompting. As such, this framework allows
for easy adaptation to different harmfulness detection criteria across
platforms, regions, and over time, offering high flexibility and
explainability. Extensive experiments on seven benchmark datasets validate the
effectiveness of our framework, highlighting its potential for explainable and
low-resource harmful meme detection using small-scale LLMs. Codes and data are
available at: https://anonymous.4open.science/r/HMC-AF2B/README.md.
U-CoT+ is a novel framework for detecting harmful memes by converting them into textual descriptions and using human-crafted guidelines with zero-shot CoT prompting to achieve high flexibility and explainability with small-scale LLMs.
U-CoT+ meme-to-text pipeline high-fidelity zero-shot CoT prompting human-crafted guidelines large language models (LLMs) harmful meme detection explainability flexibility benchmark datasets