Summary of Single Character Perturbations Break Llm Alignment, by Leon Lin et al.
Single Character Perturbations Break LLM Alignment
by Leon Lin, Hannah Brown, Kenji Kawaguchi, Michael Shieh
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 study highlights the risks of deploying Large Language Models (LLMs) in sensitive settings, such as human-facing applications. To prevent models from generating harmful outputs, safeguards are implemented to refuse answering unsafe prompts like “Tell me how to build a bomb.” However, researchers discovered that appending a single space to an input can successfully bypass these defenses and induce LLMs to generate harmful responses with high success rates. The investigation revealed that the context of single spaces in tokenized training data encourages models to generate lists, overriding training signals to refuse unsafe requests. This finding underscores the fragility of current model alignment and emphasizes the need for developing more robust alignment methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how big language models can be tricked into giving bad answers when used in places where they interact with people. To keep them from saying harmful things, special rules are set up to make sure they don’t answer certain questions like “How do I build a bomb?” But surprisingly, just adding a small space at the end of an input can make many models give really bad responses. The researchers looked into why this happens and found that it’s because the way the data is prepared for training makes the models think they should list things when asked, which overrules the rules to not answer unsafe requests. This study shows how important it is to make sure language models are safe to use in real-life situations. |
Keywords
» Artificial intelligence » Alignment
