Summary of Rainbow Teaming: Open-ended Generation Of Diverse Adversarial Prompts, by Mikayel Samvelyan et al.
Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts
by Mikayel Samvelyan, Sharath Chandra Raparthy, Andrei Lupu, Eric Hambro, Aram H. Markosyan, Manish Bhatt, Yuning Mao, Minqi Jiang, Jack Parker-Holder, Jakob Foerster, Tim Rocktäschel, Roberta Raileanu
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary As large language models become ubiquitous across various real-world applications, ensuring their robustness against adversarial attacks is crucial. Existing methods for generating adversarial prompts often focus on specific domains, lack diversity, or require extensive human annotations. To overcome these limitations, we introduce Rainbow Teaming, a novel black-box approach that generates diverse and effective adversarial prompts. By casting prompt generation as a quality-diversity problem and leveraging open-ended search, our method produces hundreds of adversarial prompts with an attack success rate exceeding 90% across state-of-the-art LLMs like Llama 2 and Llama 3. Moreover, we demonstrate that our prompts are highly transferable, allowing fine-tuning models to enhance their safety without compromising general performance or helpfulness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Rainbow Teaming is a new way to make language models stronger against bad input. Imagine trying to trick a super smart AI into saying something it shouldn’t. That’s what we’re talking about here. We found a way to make hundreds of fake prompts that can fool these AIs, making them safer and more reliable. |
Keywords
* Artificial intelligence * Fine tuning * Llama * Prompt
