Detecting Harmful Memes with Decoupled Understanding and Guided CoT
  Reasoning
Authors: name: Fengjun Pan, name: Anh Tuan Luu, name: Xiaobao Wu

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.