Summary of Mllmguard: a Multi-dimensional Safety Evaluation Suite For Multimodal Large Language Models, by Tianle Gu et al.
MLLMGuard: A Multi-dimensional Safety Evaluation Suite for Multimodal Large Language Models
by Tianle Gu, Zeyang Zhou, Kexin Huang, Dandan Liang, Yixu Wang, Haiquan Zhao, Yuanqi Yao, Xingge Qiao, Keqing Wang, Yujiu Yang, Yan Teng, Yu Qiao, Yingchun Wang
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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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 paper introduces MLLMGuard, a comprehensive safety evaluation suite for Multimodal Large Language Models (MLLMs). The authors highlight the need for robust evaluation methods to prevent potential malicious instructions and ensure the safety of MLLMs. They present a bilingual image-text evaluation dataset, inference utilities, and a lightweight evaluator, GuardRank, which achieves higher accuracy than GPT-4. The evaluation suite assesses MLLMs across five important safety dimensions (Privacy, Bias, Toxicity, Truthfulness, and Legality) and 13 advanced models, indicating that MLLMs still have a significant journey ahead before being considered safe and responsible. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to evaluate large language models. It’s like a special test for these models to make sure they’re not harmful or biased. The authors want to keep the models from doing bad things, so they created a system called MLLMGuard. This system has many parts, including a big dataset of images and text, tools to help figure out what’s going on, and a special tool that can quickly tell if something is safe or not. They tested this system with 13 different models and found that even the best ones still have a lot to learn before they’re completely trustworthy. |
Keywords
» Artificial intelligence » Gpt » Inference
