Summary of On the Role Of Attention Heads in Large Language Model Safety, by Zhenhong Zhou et al.
On the Role of Attention Heads in Large Language Model Safety
by Zhenhong Zhou, Haiyang Yu, Xinghua Zhang, Rongwu Xu, Fei Huang, Kun Wang, Yang Liu, Junfeng Fang, Yongbin Li
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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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 paper explores the connection between standard attention mechanisms and safety capability in large language models (LLMs). Despite achieving state-of-the-art performance on multiple language tasks, LLMs’ safety guardrails can be circumvented. Recent research has shown that suppressing safety representations or components compromises the safety capability of LLMs. However, the role of multi-head attention mechanisms in model functionalities has been overlooked. The proposed novel metric, Safety Head ImPortant Score (Ships), assesses individual heads’ contributions to model safety. Generalizing Ships to the dataset level and introducing the Safety Attention Head AttRibution Algorithm (Sahara) attribute critical safety attention heads inside models. Findings show that a single special attention head has a significant impact on safety, allowing aligned models to respond to 16 times more harmful queries while modifying only 0.006% of parameters. The study demonstrates that attention heads primarily function as feature extractors for safety and fine-tuned models exhibit overlapping safety heads. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how language models work safely or not. Large models are good at understanding languages, but they can be tricked into saying bad things if we’re not careful. The researchers want to know why this happens and how we can make it safer. They found that some parts of the model are more important for safety than others and that we can use these parts to make the model safer. This is important because language models are going to be used a lot in our lives, so we need to make sure they’re safe. |
Keywords
» Artificial intelligence » Attention » Multi head attention
