HumaniBench: A Human-Centric Framework for Large Multimodal Models
  Evaluation
Authors: name: Shaina Raza, name: Aravind Narayanan, name: Vahid Reza Khazaie, name: Ashmal Vayani, name: Mukund S. Chettiar, name: Amandeep Singh, name: Mubarak Shah, name: Deval Pandya

Large multimodal models (LMMs) now excel on many vision language benchmarks,
however, they still struggle with human centered criteria such as fairness,
ethics, empathy, and inclusivity, key to aligning with human values. We
introduce HumaniBench, a holistic benchmark of 32K real-world image question
pairs, annotated via a scalable GPT4o assisted pipeline and exhaustively
verified by domain experts. HumaniBench evaluates seven Human Centered AI
(HCAI) principles: fairness, ethics, understanding, reasoning, language
inclusivity, empathy, and robustness, across seven diverse tasks, including
open and closed ended visual question answering (VQA), multilingual QA, visual
grounding, empathetic captioning, and robustness tests. Benchmarking 15 state
of the art LMMs (open and closed source) reveals that proprietary models
generally lead, though robustness and visual grounding remain weak points. Some
open-source models also struggle to balance accuracy with adherence to
human-aligned principles. HumaniBench is the first benchmark purpose built
around HCAI principles. It provides a rigorous testbed for diagnosing alignment
gaps and guiding LMMs toward behavior that is both accurate and socially
responsible. Dataset, annotation prompts, and evaluation code are available at:
https://vectorinstitute.github.io/HumaniBench