VHELM: A Holistic Evaluation of Vision Language Models
Authors: name: Tony Lee, name: Haoqin Tu, name: Chi Heem Wong, name: Wenhao Zheng, name: Yiyang Zhou, name: Yifan Mai, name: Josselin Somerville Roberts, name: Michihiro Yasunaga, name: Huaxiu Yao, name: Cihang Xie, name: Percy Liang

Current benchmarks for assessing vision-language models (VLMs) often focus on
their perception or problem-solving capabilities and neglect other critical
aspects such as fairness, multilinguality, or toxicity. Furthermore, they
differ in their evaluation procedures and the scope of the evaluation, making
it difficult to compare models. To address these issues, we extend the HELM
framework to VLMs to present the Holistic Evaluation of Vision Language Models
(VHELM). VHELM aggregates various datasets to cover one or more of the 9
aspects: visual perception, knowledge, reasoning, bias, fairness,
multilinguality, robustness, toxicity, and safety. In doing so, we produce a
comprehensive, multi-dimensional view of the capabilities of the VLMs across
these important factors. In addition, we standardize the standard inference
parameters, methods of prompting, and evaluation metrics to enable fair
comparisons across models. Our framework is designed to be lightweight and
automatic so that evaluation runs are cheap and fast. Our initial run evaluates
22 VLMs on 21 existing datasets to provide a holistic snapshot of the models.
We uncover new key findings, such as the fact that efficiency-focused models
(e.g., Claude 3 Haiku or Gemini 1.5 Flash) perform significantly worse than
their full models (e.g., Claude 3 Opus or Gemini 1.5 Pro) on the bias benchmark
but not when evaluated on the other aspects. For transparency, we release the
raw model generations and complete results on our website
(https://crfm.stanford.edu/helm/vhelm/v2.0.1). VHELM is intended to be a living
benchmark, and we hope to continue adding new datasets and models over time.