Researchers have developed a machine learning technique to improve red-teaming for large language models. By training a red-team model to generate diverse prompts that elicit toxic responses from a chatbot, they achieved better coverage and effectiveness compared to human testers and other automated methods. The method provides a faster and more effective way to ensure the safety of language models, which is crucial given the rapidly changing environment of AI.
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