Summary of Red Teaming Visual Language Models, by Mukai Li and Lei Li and Yuwei Yin and Masood Ahmed and Zhenguang Liu and Qi Liu
Red Teaming Visual Language Models
by Mukai Li, Lei Li, Yuwei Yin, Masood Ahmed, Zhenguang Liu, Qi Liu
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 This paper explores the capabilities of Vision-Language Models (VLMs) in generating harmful or inaccurate content, building upon previous research on Large Language Models (LLMs). The authors introduce a novel dataset, RTVLM, which consists of 10 subtasks and 4 primary aspects to evaluate VLMs’ performance. They find that prominent open-sourced VLMs struggle with red teaming, with a significant performance gap compared to GPT-4V. Furthermore, the authors demonstrate that applying red teaming alignment to LLaVA-v1.5 using RTVLM improves model performance without compromising overall results. This study highlights the importance of red teaming alignment in current open-sourced VLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well machines can create fake or misleading content, like images or text. The authors created a special test dataset to see if these machine learning models can be tricked into producing bad results. They found that many of the popular AI models struggled with this task and didn’t do very well compared to one top-performing model. The researchers also showed that by giving some extra training to one particular model, it could improve its performance without getting worse overall. This study shows that we need to be careful about how these machine learning models are designed and trained. |
Keywords
» Artificial intelligence » Alignment » Gpt » Machine learning
