An automated LLM-judge pipeline found 58.7% of 782 sampled Custom GPTs produced at least one policy-violating response, with most violations inherited from base GPT-4 models.
From representational harms to quality-of-service harms: A case study on llama 2 safety safeguards, 2024
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Towards Safer Chatbots: Automated Policy Compliance Evaluation of Custom GPTs
An automated LLM-judge pipeline found 58.7% of 782 sampled Custom GPTs produced at least one policy-violating response, with most violations inherited from base GPT-4 models.