Detecting misogyny in multimodal content remains a notable challenge, particularly in culturally conservative and low-resource contexts like Bangladesh. While existing research has explored hate speech and general meme classification, the nuanced identification of misogyny in Bangla memes, rich in metaphor, humor, and visual-textual interplay, remains severely underexplored. To address this gap, we introduce BanMiMe, the first comprehensive Bangla misogynistic meme dataset comprising 2,000 culturally grounded samples where each meme includes misogyny labels, humor categories, metaphor localization, and detailed human-written explanations. We benchmark the various performance of open and closed-source vision-language models (VLMs) under zero-shot and prompt-based settings and evaluate their capacity for both classification and explanation generation. Furthermore, we systematically explore multiple fine-tuning strategies, including standard, data-augmented, and Chain-of-Thought (CoT) supervision. Our results demonstrate that CoT-based fine-tuning consistently enhances model performance, both in terms of accuracy and in generating meaningful explanations. We envision BanMiMe as a foundational resource for advancing explainable multimodal moderation systems in low-resource and culturally sensitive settings.

Despite significant progress in large language models (LLMs), their knowledge and evaluation continue to be centered around high-resource languages, leaving critical gaps in low-resource settings. This raises questions about how effectively LLMs handle subjects that require locally relevant knowledge. To address this challenge, we need a robust dataset that reflects the knowledge of underrepresented regions such as Bangladesh. In this paper, we present ***SOMAJGYAAN***, a Bangla multiple-choice dataset consisting of 4,234 questions, annotated across five levels of difficulty. The questions are drawn from Bangladesh’s National Curriculum and Global Studies textbooks, covering a wide range of domains including History, Geography, Economics, Social Studies, Politics and Law, and Miscellaneous topics. Difficulty levels were assigned by four expert annotators to minimize annotation bias. The experiments reveal that closed-source LLMs perform better than open-source LLMs. While fine-tuning open-source models on improves their performance, they still fall short of matching closed-source LLMs. Our findings highlight the importance of culturally grounded evaluation datasets and task-specific adaptation to improve LLM performance in low-resource language settings.

Social networks extensively feature memes, particularly cartoon images, as a prevalent form of communication often conveying complex sentiments or harmful content. Detecting such content, particularly when it involves Bengali and English text, remains a multimodal challenge. This paper introduces ***CMBan***, a novel and culturally relevant dataset of 2,641 annotated cartoon memes. It addresses meme classification based on their sentiment across five key categories: Humor, Sarcasm, Offensiveness, Motivational Content, and Overall Sentiment, incorporating both image and text features. Our curated dataset specifically aids in detecting nuanced offensive content and navigating complexities of pure Bengali, English, or code-mixed Bengali-English languages. Through rigorous experimentation involving over 12 multimodal models, including monolingual, multilingual, and proprietary architectures, and utilizing prompting methods like Chain-Of-Thought (CoT), findings suggest this cartoon-based, code-mixed meme content poses substantial understanding challenges. Experimental results demonstrate that closed models excel over open models. While the LoRA fine-tuning strategy equalizes performance across model architectures and improves classification of challenging aspects in multilingual meme contexts, this work advances meme classification by providing effective solution for detecting harmful content in multilingual meme contexts.