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arxiv: 2406.11654 · v1 · pith:ZKEHUJMK · submitted 2024-06-17 · cs.CL

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

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classification cs.CL
keywords teamingdiversityrubymemoryqualitydimensionindexrainbow
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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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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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    Persona-driven workflow and interface improve automated and human-AI red-teaming of generative AI by incorporating diverse perspectives into adversarial prompt creation.

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  3. Stable-GFlowNet: Toward Diverse and Robust LLM Red-Teaming via Contrastive Trajectory Balance

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  4. Stable-GFlowNet: Toward Diverse and Robust LLM Red-Teaming via Contrastive Trajectory Balance

    cs.LG 2026-05 unverdicted novelty 5.0

    Stable-GFlowNet improves training stability and attack diversity in LLM red-teaming by eliminating Z estimation via contrastive trajectory balance while preserving GFN optimality.

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    ToxSearch-S extends evolutionary toxicity search with embedding-driven speciation and MPI distribution, matching baseline peak toxicity while showing lower search pressure and 1.8-3.2x speedups.