13 papers
ArGuard Shared Task: Harmful Content Detection in Arabic Memes and LLM Prompts
Firoj Alam, Md. Rafiul Biswas, Mohamed Bayan Kmainasi +5
ArGuard is a shared task on harmful content detection in Arabic memes and LLM prompts. It includes two tracks: Track A focuses on multimodal hate detection in Arabic memes, while T…
ProBel: Propaganda Detection with Techniques, Spans, and Explanations
Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, Elisa Sartori +2
Propaganda detection includes several related prediction levels, ranging from sentence-level decisions to technique classification and span identification. However, it remains uncl…
AHA-Memes: A Fine-Grained Multimodal Benchmark for Understanding Hate in Arabic Memes
Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, Abul Hasnat +3
Hateful memes are a growing form of multimodal online harm, where hostile intent is often conveyed through the joint interpretation of images, text, cultural references, and implic…
Adapting Reinforcement Learning with Chain-of-Thought Supervision for Explainable Detection of Hateful and Propagandistic Memes
Mohamed Bayan Kmainasi, Mucahid Kutlu, Ali Ezzat Shahroor +2
Hateful and propagandistic memes exploit the interplay between images and text to convey harmful intent that neither modality reveals alone. Although thinking-based multimodal larg…
CritiSense: Critical Digital Literacy and Resilience Against Misinformation
Firoj Alam, Fatema Ahmad, Ali Ezzat Shahroor +5
Misinformation on social media undermines informed decision-making and public trust. Prebunking offers a proactive complement by helping users recognize manipulation tactics before…
Can Thinking Models Think to Detect Hateful Memes?
Mohamed Bayan Kmainasi, Mucahid Kutlu, Ali Ezzat Shahroor +2
Hateful memes often require compositional multimodal reasoning: the image and text may appear benign in isolation, yet their interaction conveys harmful intent. Although thinking-b…
MemeLens: Multilingual Multitask VLMs for Memes
Ali Ezzat Shahroor, Mohamed Bayan Kmainasi, Abul Hasnat +4
Memes are a dominant medium for online communication and manipulation because meaning emerges from interactions between embedded text, imagery, and cultural context. Existing meme…
OASIS: A Multilingual and Multimodal Dataset for Culturally Grounded Spoken Visual QA
Firoj Alam, Ali Ezzat Shahroor, Md. Arid Hasan +8
Large-scale multimodal models achieve strong results on tasks like Visual Question Answering (VQA), but they are often limited when queries require cultural and visual information,…
Can Large Language Models Predict the Outcome of Judicial Decisions?
Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, Amani Al-Ghraibah
Large Language Models (LLMs) have shown exceptional capabilities in Natural Language Processing (NLP) across diverse domains. However, their application in specialized tasks such a…
MemeIntel: Explainable Detection of Propagandistic and Hateful Memes
Mohamed Bayan Kmainasi, Abul Hasnat, Md Arid Hasan +2
The proliferation of multimodal content on social media presents significant challenges in understanding and moderating complex, context-dependent issues such as misinformation, ha…
PropXplain: Can LLMs Enable Explainable Propaganda Detection?
Maram Hasanain, Md Arid Hasan, Mohamed Bayan Kmainasi +4
There has been significant research on propagandistic content detection across different modalities and languages. However, most studies have primarily focused on detection, with l…
Native vs Non-Native Language Prompting: A Comparative Analysis
Mohamed Bayan Kmainasi, Rakif Khan, Ali Ezzat Shahroor +3
Large language models (LLMs) have shown remarkable abilities in different fields, including standard Natural Language Processing (NLP) tasks. To elicit knowledge from LLMs, prompts…
LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content
Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, Maram Hasanain +3
Large Language Models (LLMs) have demonstrated remarkable success as general-purpose task solvers across various fields. However, their capabilities remain limited when addressing…