Summary of Failures Are Fated, but Can Be Faded: Characterizing and Mitigating Unwanted Behaviors in Large-scale Vision and Language Models, by Som Sagar et al.
Failures Are Fated, But Can Be Faded: Characterizing and Mitigating Unwanted Behaviors in Large-Scale Vision and Language Models
by Som Sagar, Aditya Taparia, Ransalu Senanayake
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In this paper, researchers investigate the limitations of deep neural networks that perform well on many tasks but still fail in certain aspects, such as accuracy, social biases, and alignment with human values. To address these issues, they propose a post-hoc method using deep reinforcement learning to explore and construct the failure landscape in pre-trained discriminative and generative models. The approach utilizes limited human feedback to restructure the failure landscape, making it more desirable by moving away from discovered failure modes. The authors demonstrate the effectiveness of this method across various computer vision, natural language processing, and vision-language tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how deep neural networks can sometimes fail in ways that are important for us to know about. For example, they might not be fair or accurate. Researchers want to understand these failures so they can fix them before using the models in real-life applications. To do this, they created a new way to explore and change the “failure landscape” of pre-trained models. This method uses something called deep reinforcement learning and some help from humans. The results show that this approach works well for different types of tasks like image recognition and language processing. |
Keywords
» Artificial intelligence » Alignment » Natural language processing » Reinforcement learning
