摘要：Meme 已成为人们分享其对当代社会与政治问题观点的有力工具。其匿名性以及病毒式传播能力，也使其成为传播仇恨的强大媒介。识别这类复杂且依赖语境的仇恨言论仍然非常困难。尽管视觉语言模型（VLM）在多模态任务中表现优异，但它们往往忽略语境、反讽及其他在识别仇恨 Meme 中发挥关键作用的细微线索。在本研究中，我们对四种最先进的 VLM——LLaVA-7B、Qwen-VL、GPT-4o mini 和 Claude 3 Haiku——进行了定性分析。我们在零样本和少样本提示条件下评估这些模型，以考察语境框定如何影响其输出。我们的分析超越了简单的分类准确率，重点对模型生成的理由进行定性评估，从而更深入地理解模型在处理仇恨 Meme 时的思考过程与约束。

Abstract:Memes have turned out to be a powerful tool through which individuals share their ideas concerning contemporary social and political problems. Their anonymity, as well as their ability to go viral, make them a powerful medium for spreading hate. It remains very difficult to identify such complex and context-dependent hate speech. Although they display excellent performance on multimodal tasks, vision-language models (VLMs) tend to ignore context, irony, and other subtle cues that play a key role in identifying hateful memes. In this work, we present a qualitative analysis of four state-of-the-art VLMs: LLaVA-7B, Qwen-VL, GPT-4o mini, and Claude 3 Haiku. We evaluate these models under zero-shot and few-shot prompting to examine how contextual framing influences their outputs. Our analysis goes beyond simple classification accuracy and focuses on a qualitative evaluation of the models' generated justifications, providing a more in-depth understanding of their thought processes and constraints when dealing with hateful memes.