Summary of Multitrust: a Comprehensive Benchmark Towards Trustworthy Multimodal Large Language Models, by Yichi Zhang et al.
MultiTrust: A Comprehensive Benchmark Towards Trustworthy Multimodal Large Language Models
by Yichi Zhang, Yao Huang, Yitong Sun, Chang Liu, Zhe Zhao, Zhengwei Fang, Yifan Wang, Huanran Chen, Xiao Yang, Xingxing Wei, Hang Su, Yinpeng Dong, Jun Zhu
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Multimodal Large Language Models (MLLMs) have exceptional capabilities across various tasks, but they still face significant trustworthiness challenges. A comprehensive and unified benchmark is lacking to offer thorough insights into future improvements. This work establishes MultiTrust, the first such benchmark on the trustworthiness of MLLMs across five primary aspects: truthfulness, safety, robustness, fairness, and privacy. The benchmark employs a rigorous evaluation strategy that addresses both multimodal risks and cross-modal impacts, encompassing 32 diverse tasks with self-curated datasets. Extensive experiments with 21 modern MLLMs reveal previously unexplored trustworthiness issues and risks, highlighting the complexities introduced by multimodality and underscoring the necessity for advanced methodologies to enhance reliability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multimodal Large Language Models (MLLMs) are very powerful tools that can do many things. However, they have a problem: people don’t always trust them. This is important because we need to make sure these models are reliable and safe to use. The authors of this paper created a special tool called MultiTrust to help evaluate the trustworthiness of MLLMs. They looked at five different aspects: how truthful the model is, whether it’s safe to use, how well it works in different situations, whether it treats everyone fairly, and whether it keeps private information private. They found that some models are better than others when it comes to these things. |
