Summary of Controllable Safety Alignment: Inference-time Adaptation to Diverse Safety Requirements, by Jingyu Zhang et al.
Controllable Safety Alignment: Inference-Time Adaptation to Diverse Safety Requirements
by Jingyu Zhang, Ahmed Elgohary, Ahmed Magooda, Daniel Khashabi, Benjamin Van Durme
First submitted to arxiv on: 11 Oct 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 This paper proposes an alternative approach to ensuring the safety alignment of large language models (LLMs). The current method involves blocking any content deemed unsafe by the model provider, but this one-size-fits-all approach fails to account for cultural and regional variations in social norms. Moreover, users may have different safety needs, making a static safety standard too restrictive and costly. To address these limitations, the paper suggests a more flexible and adaptive safety alignment framework that takes into consideration diverse user needs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) need to be safe for everyone! Right now, if a model thinks something is unsafe, it just won’t show you. But this approach doesn’t work well when different cultures have different ideas about what’s okay and what’s not. It also makes the model too strict and expensive to use. |
Keywords
» Artificial intelligence » Alignment
