6 papers
Can LLMs Follow the Pulse of a Crisis? Evaluating Crisis Sentiment in Bangladesh's July Uprising
Md. Samiul Alim, Mahir Shahriar Tamim, Tanvir Ahmed Khan +4
Crisis sentiment analysis is especially challenging for low-resource languages such as Bangla, where language, context, and public reaction shift rapidly. We introduce UNRESTSENT20…
CAT-GS: Balanced Multimodal Learning via Calibrated Gating and Fusion Surgery
Mahir Shahriar Tamim, Sharjil Khan, Md. Samiul Alim +3
End-to-end training of multimodal neural networks often exhibits unstable neural dynamics characterized by three coupled failure modes that degrade learning: (i) modality imbalance…
Skip What You Can Predict: Predictive Repositioning for Policy Optimization for Efficient LLM Training
Ismam Nur Swapnil, Aranya Saha, Tanvir Ahmed Khan +3
Reinforcement learning with verifiable rewards (RLVR) can improve the reasoning ability of large language models, but repeatedly updating a policy on the same rollout batch is expe…
GRPO++: Enhancing Dermatological Reasoning under Low Resource Settings
Ismam Nur Swapnil, Aranya Saha, Tanvir Ahmed Khan +1
Vision-Language Models (VLMs) show promise in medical image analysis, yet their capacity for structured reasoning in complex domains like dermatology is often limited by data scarc…
CLARIFY: A Specialist-Generalist Framework for Accurate and Lightweight Dermatological Visual Question Answering
Aranya Saha, Tanvir Ahmed Khan, Ismam Nur Swapnil +1
Vision-language models (VLMs) have shown significant potential for medical tasks; however, their general-purpose nature can limit specialized diagnostic accuracy, and their large s…
Compression Strategies for Efficient Multimodal LLMs in Medical Contexts
Tanvir A. Khan, Aranya Saha, Ismam N. Swapnil +1
Multimodal Large Language Models (MLLMs) hold huge potential for usage in the medical domain, but their computational costs necessitate efficient compression techniques. This paper…