Recent advancements in Large Language Models (LLMs) and Large Vision Language Models (LVLMs) have enabled general-purpose systems to demonstrate promising capabilities in complex reasoning tasks, including those in the medical domain. However, their evaluation has predominantly focused on high-resource languages, leaving low-resource contexts like Bangla underexplored. To address this gap, we introduce BanglaMedVQA, a multilingual Medical Visual Question Answering (VQA) dataset comprising clinically validated image–question–answer pairs, along with a comprehensive evaluation of current LVLMs on this resource. We rigorously evaluate nine state-of-the-art LVLMs using zero-shot, Chain-of-Thought (CoT), and LoRA fine-tuning strategies. Our results reveal a clear performance disparity: models perform well on generalized visual tasks but struggle with fine-grained diagnostic reasoning, achieving surprisingly low accuracy in specialized categories. While fine-tuning significantly improves overall accuracy, especially for Qwen2.5-VL and MedGemma 4B, limitations in specialized medical reasoning persist. Our work provides a foundation for future research in Bangla medical VQA.

We present a novel Bangla Dialect Dataset comprising 600 annotated instances across four major dialects: Chattogram, Barishal, Sylhet, and Noakhali. The dataset was constructed from YouTube comments spanning diverse domains to capture authentic dialectal variations in informal online communication. Each instance includes the original dialectical text, its standard Bangla translation, and sentiment labels (Positive and Negative). We benchmark several state-of-the-art large language models on dialect-to-standard translation and sentiment analysis tasks using zero-shot and few-shot prompting strategies. Our experiments reveal that transliteration significantly improves translation quality for closed-source models, with GPT-4o-mini achieving the highest BLEU score of 0.343 in zero-shot with transliteration. For sentiment analysis, GPT-4o-mini demonstrates perfect precision, recall, and F1 scores (1.000) in few-shot settings. This dataset addresses the critical gap in resources for low-resource Bangla dialects and provides a foundation for developing dialect-aware NLP systems.

Adapting large pre-trained language models (LLMs) to downstream tasks typically requires fine-tuning, but fully updating all parameters is computationally prohibitive. Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation (LoRA) reduce this cost by updating a small subset of parameters. However, the standard approach of jointly training LoRA adapters and a new classifier head from a cold start can lead to training instability, as the classifier chases shifting feature representations. To address this, we propose LP-FT-LoRA, a novel three-stage training framework that decouples head alignment from representation learning to enhance stability and performance. Our framework first aligns the classifier head with the frozen backbone via linear probing, then trains only the LoRA adapters to learn task-specific features, and finally performs a brief joint refinement of the head and adapters. We conduct extensive experiments on five Bangla NLP benchmarks across four open-weight compact transformer models. The results demonstrate that LP-FT-LoRA consistently outperforms standard LoRA fine-tuning and other baselines, achieving state-of-the-art average performance and showing improved generalization on out-of-distribution datasets.