Summary of Navigating the Safety Landscape: Measuring Risks in Finetuning Large Language Models, by Shengyun Peng et al.
Navigating the Safety Landscape: Measuring Risks in Finetuning Large Language Models
by ShengYun Peng, Pin-Yu Chen, Matthew Hull, Duen Horng Chau
First submitted to arxiv on: 27 May 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 The proposed research aims to measure the risks associated with finetuning large language models (LLMs) and ensure their safety alignment with human preferences. The study discovers a novel phenomenon called the “safety basin” in popular open-source LLMs, where random perturbations to model weights maintain the original aligned model’s safety within a local neighborhood. However, outside this region, safety is fully compromised. This finding inspires the development of a new VISAGE safety metric and visualizing the safety landscape of the aligned model to understand how finetuning compromises safety by dragging the model away from the safety basin. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are powerful tools that can help us communicate more effectively, but they need to be safe. The problem is that these models can easily learn bad behavior if someone trains them with the wrong data. Researchers have discovered a new way to measure the risks of training LLMs and make sure they behave well. They found something called the “safety basin” – if you slightly change the model’s weights, it stays safe, but if you go too far, it becomes unsafe. This helps us understand how to keep these models safe and what kind of prompts we should use. |
Keywords
» Artificial intelligence » Alignment
