Summary of Legend: Leveraging Representation Engineering to Annotate Safety Margin For Preference Datasets, by Duanyu Feng et al.
Legend: Leveraging Representation Engineering to Annotate Safety Margin for Preference Datasets
by Duanyu Feng, Bowen Qin, Chen Huang, Youcheng Huang, Zheng Zhang, Wenqiang Lei
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 framework, Legend, aims to develop a cost-efficient method for creating margin-enhanced preference datasets, which are crucial for distinguishing between responses with subtle safety differences. The framework leverages representation engineering and semantic distances to annotate margins automatically. In the context of large language models (LLMs), Legend can improve reward modeling and harmless alignment. The approach requires only inference time, making it efficient and scalable for practical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Legend is a new method for creating preference datasets that helps computers understand what’s safe or not. It works by finding special directions in the computer’s “brain” that represent safety. This allows Legend to compare two responses and say which one is safer. Legend is useful because it doesn’t need extra training, just a little extra processing time. This makes it easier to use in real-life situations where computers need to have safe conversations. |
Keywords
» Artificial intelligence » Alignment » Inference
