UkrMeme：用视觉语言模型检测乌克兰梗图中的说服技巧
UkrMeme: Multimodal Explainable Persuasion Detection in Ukrainian Memes
乌克兰团队把 200 张战时梗图做成了带说服技巧标注的数据集，还测了各家视觉语言模型靠不靠得住，做内容审核或多模态研究的朋友可以看看。
乌克兰研究团队发布了 UkrMeme 数据集，包含 200 张俄乌战争期间真实流传的乌克兰语梗图，标注了其中的说服技巧。同时推出 PTM-UA，将 Propaganda 950 基准的一个子集翻译为乌克兰语，并保留原图。实验用零样本提示让视觉语言模型分别处理图像、文本和多模态输入，以分层 F1 指标评估检测效果。论文还比较了 Layer-wise Relevance Propagation 与模型自带的 token 归因两种可解释方法，分析各自强调的证据类型差异。
UkrMeme: Multimodal Explainable Persuasion Detection in Ukrainian Memes
Memes play a central role in modern online communication, especially in contexts of political conflict where visual and textual cues combine to convey persuasive messages. We study persuasive technique detection in Ukrainian memes using vision-language models (VLMs). We introduce UkrMeme, a dataset of 200 organically produced Ukrainian wartime memes annotated for persuasion techniques, and PTM-UA, a Ukrainian translation of a subset of the Propaganda 950 benchmark that preserves original visual content while translating meme text. We evaluate VLMs in a zero-shot prompting setup across image, text, and multimodal inputs using hierarchical F1 metrics. Finally, we compare Layer-wise Relevance Propagation (LRP) and model-generated token attributions and analyze the semantic categories of evidence emphasized by each explanation method.
