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Red-Teaming Large Language Models
This Hugging Face blog post introduces red-teaming as a safety evaluation methodology for large language models, explaining how adversarial testing can surface harmful outputs, biases, and failure modes before deployment. It covers techniques for systematically probing LLMs to elicit problematic behaviors and discusses the role of red-teaming in responsible AI development. The post serves as an educational overview aimed at practitioners working on LLM safety.