📝 SummarXiv

Abstract

Large multimodal models (LMMs) have been widely tested on tasks like visual question answering (VQA), image captioning, and grounding, but lack rigorous evaluation for alignment with human-centered (HC) values such as fairness, ethics, and inclusivity. To address this gap, we introduce \textbf{HumaniBench}, a novel benchmark of 32,000 real-world image-question pairs and an evaluation suite. Labels are generated via an AI-assisted pipeline and validated by experts. HumaniBench assesses LMMs across seven key alignment principles: fairness, ethics, empathy, inclusivity, reasoning, robustness, and multilinguality, through diverse open-ended and closed-ended VQA tasks. Grounded in AI ethics and real-world needs, these principles provide a holistic lens for societal impact. Benchmarking results on different LMM shows that proprietary models generally lead in reasoning, fairness, and multilinguality, while open-source models excel in robustness and grounding. Most models struggle to balance accuracy with ethical and inclusive behavior. Techniques like Chain-of-Thought prompting and test-time scaling improve alignment. As the first benchmark tailored for HC alignment, HumaniBench offers a rigorous testbed to diagnose limitations, and promote responsible LMM development. All data and code are publicly available for reproducibility. Keywords: HumaniBench, vision-language models, responsible AI benchmark, AI alignment evaluation, AI ethics assessment, fairness in AI models, visual question answering (VQA) benchmark, image captioning evaluation, visual grounding tasks, trustworthy AI models, Chain-of-Thought prompting, test-time scaling, ethical AI development tools.