Summary of Vhelm: a Holistic Evaluation Of Vision Language Models, by Tony Lee et al.
VHELM: A Holistic Evaluation of Vision Language Models
by Tony Lee, Haoqin Tu, Chi Heem Wong, Wenhao Zheng, Yiyang Zhou, Yifan Mai, Josselin Somerville Roberts, Michihiro Yasunaga, Huaxiu Yao, Cihang Xie, Percy Liang
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Holistic Evaluation of Vision Language Models (VHELM) framework assesses the capabilities of vision-language models (VLMs) across 9 critical aspects: visual perception, knowledge, reasoning, bias, fairness, multilinguality, robustness, toxicity, and safety. VHELM aggregates various datasets to cover one or more of these aspects, providing a comprehensive, multi-dimensional view of the VLMs’ capabilities. The framework standardizes evaluation parameters, prompting methods, and metrics to enable fair comparisons across models. Initial results evaluate 22 VLMs on 21 existing datasets, revealing new findings such as efficiency-focused models performing worse than full models on bias benchmarking. The proposed benchmark aims to provide a living evaluation tool, continually adding new datasets and models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to test how well vision-language models can understand pictures and text together. Currently, we only look at how good these models are at doing things like recognizing objects or answering questions. But that’s not the whole story. These models should also be fair, able to work with different languages, and not say mean or harmful things. The new framework, called VHELM, looks at all of these aspects together. It uses many datasets to test how well the models do on each one. This helps us understand which models are really good at what they do. |
Keywords
» Artificial intelligence » Prompting
