Summary of Multi-round Jailbreak Attack on Large Language Models, by Yihua Zhou et al.
Multi-round jailbreak attack on large language models
by Yihua Zhou, Xiaochuan Shi
First submitted to arxiv on: 15 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 research paper proposes a solution to ensure the safety and alignment of large language models (LLMs) with human values. Specifically, it addresses “jailbreak” attacks on LLMs, which aim to induce the generation of toxic content through carefully crafted prompts. The study focuses on developing novel methods to detect and prevent these attacks, highlighting their limitations in evading static rule-based filters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps keep language models safe by stopping them from generating bad responses when given tricky questions. Right now, some models can be tricked into saying harmful things if the question is worded just right. The researchers want to find ways to prevent this from happening and make sure the model stays on track. |
Keywords
» Artificial intelligence » Alignment
