Large Vision-Language Models (LVLMs) have achieved strong performance on vision-language tasks, particularly Visual Question Answering (VQA). While prior work has explored unimodal biases in VQA, the problem of selection bias in Multiple-Choice Question Answering (MCQA), where models may favor specific option tokens (e.g., “A”) or positions, remains underexplored. In this paper, we investigate both the presence and nature of selection bias in LVLMs through fine-grained MCQA benchmarks spanning easy, medium, and hard difficulty levels, defined by the semantic similarity of the options. We further propose an inference-time logit-level debiasing method that estimates an ensemble bias vector from general and contextual prompts and applies confidence-adaptive corrections to the model’s output. Our method mitigates bias without retraining and is compatible with frozen LVLMs. Extensive experiments across several state-of-the-art models reveal consistent selection biases that intensify with task difficulty, and show that our mitigation approach significantly reduces bias while improving accuracy in challenging settings. This work offers new insights into the limitations of LVLMs in MCQA and presents a practical approach to improve their robustness in fine-grained visual reasoning. Datasets and code are available at: https://github.com/Atabuzzaman/Selection-Bias-of-LVLMs

Emotion recognition for high-resource languages has progressed significantly. However, resource-constrained languages such as Bengali have not advanced notably due to the lack of large benchmark datasets. Besides this, the need for more Bengali language processing tools makes the emotion recognition task more challenging and complicated. Therefore, we developed the largest dataset in this paper, consisting of almost 12k Bengali texts with six basic emotions. Then, we conducted experiments on our dataset to establish the baseline performance applying machine learning, deep learning, and transformer-based models as emotion classifiers. The experimental results demonstrate that the models achieved promising performance in Bengali emotion recognition.