Detecting hate speech in memes is challenging due to their multimodal nature and subtle, culturally grounded cues such as sarcasm and context. While recent vision-language models (VLMs) enable joint reasoning over text and images, end-to-end prompting can be brittle, as a single prediction must resolve target, stance, implicitness, and irony. These challenges are amplified in multilingual settings. We propose a prompted weak supervision (PWS) approach that decomposes meme understanding into targeted, question-based labeling functions with constrained answer options for homophobia and transphobia detection in the LT-EDI 2026 shared task. Using a quantized Qwen3-VLM to extract features by answering targeted questions, our method outperforms direct VLM classification, with substantial gains for Chinese and Hindi, ranking 1st in English, 2nd in Chinese, and 3rd in Hindi. Iterative refinement via error-driven LF expansion and feature pruning reduces redundancy and improves generalization. Our results highlight the effectiveness of prompted weak supervision for multilingual multimodal hate speech detection.

翻译：由于模因的多模态特性以及其中蕴含的讽刺、语境等微妙文化线索，检测其中的仇恨言论极具挑战性。尽管近期视觉语言模型能够实现对文本和图像的联合推理，但端到端的提示方法可能不够鲁棒，因为单一预测必须同时解决目标、立场、隐晦性和讽刺性等问题。在多语言环境下，这些挑战更加突出。我们提出了一种基于提示的弱监督方法，该方法将模因理解分解为针对性的、基于问题的标注函数，并针对LT-EDI 2026共享任务中的恐同和跨性别恐惧检测设置了受限的答案选项。通过使用量化的Qwen3-VLM模型回答针对性问题来提取特征，我们的方法优于直接的VLM分类，在中文和印地语上取得了显著提升，并在英语、中文和印地语中分别排名第1、第2和第3。通过基于误差驱动的标注函数扩展和特征剪枝进行迭代优化，减少了冗余并提升了泛化能力。我们的研究结果突显了基于提示的弱监督方法在多语言多模态仇恨言论检测中的有效性。