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Quality-Diversity Red-Teaming: Automated Generation of High-Quality and Diverse Attackers for Large Language Models
arXiv cs.LG — Machine Learning
Factual evidence
What the source reports
Researchers propose Quality-Diversity Red-Teaming, an automated framework to generate diverse adversarial prompts for safety testing LLMs.
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- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.LG — Machine Learning · RESEARCH
- Published by source
- 22 October 2026
- Collected by OneBench
- 4 Aug 2026, 15:55 UK
Stored source excerpt
arXiv:2506.07121v2 Announce Type: replace Abstract: Ensuring the safety and robustness of large language models (LLMs) is a fundamental challenge and a critical prerequisite for the…
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