Automated LLM red teaming gets a learning layer
Automated red teaming of large language models has settled into a familiar pattern over the past two years. An attacker model generates jailbreak attempts against a target model, an evaluator scores the results, and the cycle repeats. Two approaches dominate. One asks the attacker to invent strategies through trial and error, which tends to produce a narrow band of successful attacks. The other, exemplified by the WildTeaming framework, draws from large open-source pools of harmful … More
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