Summary of How Alignment and Jailbreak Work: Explain Llm Safety Through Intermediate Hidden States, by Zhenhong Zhou et al.
How Alignment and Jailbreak Work: Explain LLM Safety through Intermediate Hidden States
by Zhenhong Zhou, Haiyang Yu, Xinghua Zhang, Rongwu Xu, Fei Huang, Yongbin Li
First submitted to arxiv on: 9 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Computers and Society (cs.CY)
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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 abstract presents a research paper that investigates the mechanisms of safety alignment and jailbreak in large language models (LLMs). The study confirms that LLMs learn ethical concepts during pre-training, which enables them to identify malicious and normal inputs early on. However, the alignment process refines these concepts into specific reject tokens for safe generations, making it challenging to understand the inner workings of LLM safety. The paper employs weak classifiers to explain LLM safety through intermediate hidden states, demonstrating that jailbreak disturbs the transformation of early unethical classification into negative emotions. The study conducts experiments on models from 7B to 70B across various model families, providing a new perspective on LLM safety and reducing concerns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are powerful tools that can generate helpful text or even create art. But sometimes, these models can be tricked into producing harmful content by giving them bad instructions. This is called “jailbreaking” the model. Researchers want to understand how this works so they can make sure the models stay safe and don’t cause harm. One way they’re doing this is by looking at what’s happening inside the model as it makes decisions. They’ve found that the model learns ethical concepts during training, which helps it identify good or bad input. But when someone tries to jailbreak the model, it messes up this process and makes the model produce harmful content. |
Keywords
» Artificial intelligence » Alignment » Classification
