Summary of Learning Diverse Attacks on Large Language Models For Robust Red-teaming and Safety Tuning, by Seanie Lee et al.
Learning diverse attacks on large language models for robust red-teaming and safety tuning
by Seanie Lee, Minsu Kim, Lynn Cherif, David Dobre, Juho Lee, Sung Ju Hwang, Kenji Kawaguchi, Gauthier Gidel, Yoshua Bengio, Nikolay Malkin, Moksh Jain
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 A crucial step in deploying large language models (LLMs) safely is identifying “red-teaming” prompts, which elicit harmful responses. Developing effective protection against various attack prompts requires discovering diverse attacks. Traditional automated red-teaming methods use reinforcement learning to fine-tune an attacker model, but existing approaches suffer from mode collapse or ineffectiveness. The authors propose GFlowNet fine-tuning and a secondary smoothing phase to train the attacker model, generating diverse and effective attack prompts that work against various target LLMs. This approach is demonstrated to be robust against attacks from other methods, making it a crucial step in ensuring the safe deployment of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a powerful language tool that can respond to questions or write text. To keep this tool safe and prevent bad things from happening, we need to test its limits. This is called “red-teaming.” The problem is that traditional methods for doing this don’t work well because they tend to get stuck in one way of thinking. The authors have developed a new approach that can create many different ways to test the tool’s limits and make sure it stays safe. |
Keywords
» Artificial intelligence » Fine tuning » Reinforcement learning
