arXiv:2505.09591v2 Announce Type: replace 
Abstract: Despite remarkable progress in recent years, vision language models (VLMs) remain prone to overconfidence and hallucinations on tasks such as Visual Question Answering (VQA) and Visual Reasoning. Bayesian methods can potentially improve reliability by helping models selectively predict, that is, models respond only when they are sufficiently confident. Unfortunately, Bayesian methods are often assumed to be costly and ineffective for large models, and so far there exists little evidence to show otherwise, especially for multimodal applications. Here, we show the effectiveness and competitive edge of variational Bayes for selective prediction in VQA for the first time. We build on recent advances in variational methods for deep learning and propose an extension called "Variational VQA". This method improves calibration and yields significant gains for selective prediction on VQA and Visual Reasoning, particularly when the error tolerance is low ($\leq 1\%$). Often, just one posterior sample can yield more reliable answers than those obtained by models trained with AdamW. In addition, we propose a new risk-averse selector that outperforms standard sample averaging by considering the variance of predictions. Overall, we present compelling evidence that variational learning is a viable option to make large VLMs safer and more trustworthy.

تقدم دراسة حديثة نهجًا جديدًا للإجابة على الأسئلة البصرية (VQA) يستخدم طرق بايزيان لتحسين موثوقية نماذج اللغة البصرية. هذا مهم لأنه يعالج المشكلات الشائعة مثل الإفراط في الثقة والهلاوس في ردود الذكاء الاصطناعي، مما يسمح للنماذج بإجراء التنبؤات فقط عندما تكون واثقة. من خلال تحسين عملية اتخاذ القرار في الذكاء الاصطناعي، يمكن أن تؤدي هذه الأبحاث إلى تطبيقات أكثر دقة وموثوقية في مجالات متنوعة، من التعليم إلى خدمة العملاء.

Un estudio reciente presenta un nuevo enfoque para la Respuesta a Preguntas Visuales (VQA) que utiliza métodos bayesianos para mejorar la fiabilidad de los modelos de lenguaje visual. Esto es significativo porque aborda los problemas comunes de sobreconfianza y alucinaciones en las respuestas de la IA, permitiendo que los modelos hagan predicciones solo cuando están seguros. Al mejorar el proceso de toma de decisiones en la IA, esta investigación podría llevar a aplicaciones más precisas y confiables en diversos campos, desde la educación hasta el servicio al cliente.

Une étude récente présente une nouvelle approche pour la réponse aux questions visuelles (VQA) qui utilise des méthodes bayésiennes pour améliorer la fiabilité des modèles de langage visuel. Cela est important car cela aborde les problèmes courants de surconfiance et d'hallucinations dans les réponses de l'IA, permettant aux modèles de faire des prédictions uniquement lorsqu'ils sont confiants. En améliorant le processus de prise de décision dans l'IA, cette recherche pourrait conduire à des applications plus précises et fiables dans divers domaines, de l'éducation au service client.

A recent study introduces a new approach to Visual Question Answering (VQA) that leverages Bayesian methods to enhance the reliability of vision language models. This is significant because it addresses the common issues of overconfidence and hallucinations in AI responses, allowing models to make predictions only when they are confident. By improving the decision-making process in AI, this research could lead to more accurate and trustworthy applications in various fields, from education to customer service.

Variational Visual Question Answering for Uncertainty-Aware Selective Prediction

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Variational Visual Question Answering for Uncertainty-Aware Selective Prediction

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