11 papers
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…
NativQA Framework: Enabling LLMs and VLMs with Native, Local, and Everyday Knowledge
Firoj Alam, Md Arid Hasan, Sahinur Rahman Laskar +3
The rapid progress of large language models (LLMs) raises concerns about cultural bias, fairness, and performance in diverse languages and underrepresented regions. Addressing thes…
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…
Measuring Political Stance and Consistency in Large Language Models
Salah Feras Alali, Mohammad Nashat Maasfeh, Mucahid Kutlu +1
With the incredible advancements in Large Language Models (LLMs), many people have started using them to satisfy their information needs. However, utilizing LLMs might be problemat…
MultiAIGCD: A Comprehensive dataset for AI Generated Code Detection Covering Multiple Languages, Models,Prompts, and Scenarios
Basak Demirok, Mucahid Kutlu, Selin Mergen
As large language models (LLMs) rapidly advance, their role in code generation has expanded significantly. While this offers streamlined development, it also creates concerns in ar…
TurQUaz at CheckThat! 2025: Debating Large Language Models for Scientific Web Discourse Detection
Tarık Saraç, Selin Mergen, Mucahid Kutlu
In this paper, we present our work developed for the scientific web discourse detection task (Task 4a) of CheckThat! 2025. We propose a novel council debate method that simulates s…