HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluation

• URL: http://arxiv.org/abs/2505.11454v5
• Date: Sun, 09 Nov 2025 23:48:51 GMT
• Title: HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluation
• Authors: Shaina Raza, Aravind Narayanan, Vahid Reza Khazaie, Ashmal Vayani, Mukund S. Chettiar, Amandeep Singh, Mubarak Shah, Deval Pandya,
• Abstract summary: Large multimodal models (LMMs) have achieved impressive performance on vision tasks such as visual question answering (VQA), image captioning, and visual grounding.<n>HumaniBench is a benchmark comprising 32,000 real-world image-question pairs and an accompanying evaluation suite.<n>It assesses LMMs across seven key alignment principles: fairness, ethics, empathy, inclusivity, reasoning, robustness, and multilinguality.
• Score: 44.973773675725674
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large multimodal models (LMMs) have achieved impressive performance on vision-language tasks such as visual question answering (VQA), image captioning, and visual grounding; however, they remain insufficiently evaluated for alignment with human-centered (HC) values such as fairness, ethics, and inclusivity. To address this gap, we introduce HumaniBench, a comprehensive benchmark comprising 32,000 real-world image-question pairs and an accompanying evaluation suite. Using a semi-automated annotation pipeline, each sample is rigorously validated by domain experts to ensure accuracy and ethical integrity. HumaniBench assesses LMMs across seven key alignment principles: fairness, ethics, empathy, inclusivity, reasoning, robustness, and multilinguality through a diverse set of open- and closed-ended VQA tasks. Grounded in AI ethics theory and real-world social contexts, these principles provide a holistic lens for examining human-aligned behavior. Benchmarking results reveal distinct behavioral patterns: certain model families excel in reasoning, fairness, and multilinguality, while others demonstrate greater robustness and grounding capability. However, most models still struggle to balance task accuracy with ethical and inclusive responses. Techniques such as chain-of-thought prompting and test-time scaling yield measurable alignment gains. As the first benchmark explicitly designed for HC evaluation, HumaniBench offers a rigorous testbed to diagnose limitations, quantify alignment trade-offs, and promote the responsible development of large multimodal models. All data and code are publicly released to ensure transparency and reproducibility. https://vectorinstitute.github.io/HumaniBench/

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