Ruby Teaming: Improving Quality Diversity Search with Memory for Automated Red Teaming

Han, Vernon Toh Yan, Bhardwaj, Rishabh, Poria, Soujanya

arXiv.org Artificial Intelligence 

We propose Ruby Teaming, a method that improves on Rainbow Teaming by including a memory cache as its third dimension. The memory dimension provides cues to the mutator to yield better-quality prompts, both in terms of attack success rate (ASR) and quality diversity. The prompt archive generated by Ruby Teaming has an ASR of 74%, which is 20% higher than the baseline. In terms of quality diversity, Ruby Teaming outperforms Rainbow Teaming by 6% and 3% on Shannon's Evenness Index (SEI) and Simpson's Diversity Index (SDI), respectively.

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