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

arXiv:2505.11454 · cs.CV, cs.AI · Submitted 2025-05-16 · Read on arXiv

cs.CV, cs.AI

Submitted: 2025-05-16

Updated: 2026-09-07

Code: https://github.com/VectorInstitute/HumaniBench

Project page: https://vectorinstitute.github.io/humanibench/https://huggingface.co/vector-institute/HumaniBenchhttps://github.com/VectorInstitute/HumaniBench

Terminology

• Accuracy — 73× in this paper · explained in 1 episode
• Grounding — 48× in this paper · explained in 3 episodes
• Robustness — 43× in this paper · explained in 5 episodes
• Alignment — 39× in this paper · explained in 1 episode
• Hallucination — 38× in this paper · explained in 3 episodes
• Benchmark — 34× in this paper · explained in 2 episodes
• Visual Grounding — 27× in this paper · explained in 2 episodes
• Interpretability — 11× in this paper · explained in 15 episodes
• Attention Ratio — 8× in this paper
• Core — 8× in this paper · explained in 2 episodes
• Consistency — 6× in this paper · explained in 3 episodes
• Verification — 6× in this paper · explained in 2 episodes
• Aggregation — 5× in this paper · explained in 1 episode
• Vision-language models — 5× in this paper · explained in 2 episodes
• Test-time Scaling — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Ablation Studies — 3× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• Benchmarking — 3× in this paper · explained in 3 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Foundation Models — 3× in this paper · explained in 7 episodes
• Human-in-the-Loop — 3× in this paper · explained in 3 episodes
• Instruction Tuning — 3× in this paper · explained in 2 episodes
• Unified Framework — 3× in this paper · explained in 5 episodes
• Accountability — 2× in this paper · explained in 2 episodes
• Chain-of-Thought Prompting — 2× in this paper · explained in 2 episodes
• Commentary — 2× in this paper · explained in 2 episodes
• Distribution Shift — 2× in this paper · explained in 6 episodes
• Evaluation Dimensions — 2× in this paper · explained in 3 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Agentic Reasoning — 1× in this paper · explained in 1 episode
• Agentic Workflows — 1× in this paper · explained in 1 episode
• Alignment Dimensions — 1× in this paper · explained in 1 episode
• Chain-of-Thought (CoT) — 1× in this paper · explained in 4 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Human-Centric Artificial Intelligence — 1× in this paper
• International Electrotechnical Commission — 1× in this paper
• LLM judge — 1× in this paper · explained in 2 episodes
• Low-Resource Languages — 1× in this paper · explained in 4 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Multimodal Inputs — 1× in this paper · explained in 2 episodes
• Plain and Chain-of-Thought — 1× in this paper

Sources

• MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs
• The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence
• Examining Gender and Racial Bias in Large Vision-Language Models Using a Novel Dataset of Parallel Images
• VLBiasBench: A Comprehensive Benchmark for Evaluating Bias in Large Vision-Language Model
• Red Teaming Visual Language Models
• Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision
• MM-SpuBench: Towards Better Understanding of Spurious Biases in Multimodal LLMs
• HERM: Benchmarking and Enhancing Multimodal LLMs for Human-Centric Understanding
• AlignMMBench: Evaluating Chinese Multimodal Alignment in Large Vision-Language Models
• MVP-Bench: Can Large Vision--Language Models Conduct Multi-level Visual Perception Like Humans?
• SONIC-O1: A Real-World Benchmark for Evaluating Multimodal Large Language Models on Audio-Video Understanding
• GPT-4o System Card
• Qwen2.5-VL Technical Report
• Phi-4 Technical Report
• Gemma 3 Technical Report
• CogVLM2: Visual Language Models for Image and Video Understanding
• Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models
• Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling
• The Llama 3 Herd of Models
• DeepSeek-VL: Towards Real-World Vision-Language Understanding

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