Summary:

This paper presents a thorough evaluation of the trustworthiness of Generative Pre-trained Transformer (GPT) models, particularly GPT-4 and GPT-3.5. The evaluation covers areas like toxicity, stereotype bias, adversarial robustness, and more. The findings reveal vulnerabilities that affect the models’ ability to generate unbiassed and reliable outputs.

Key Points:

• Comprehensive trustworthiness evaluation focusing on toxicity, stereotype bias, and adversarial robustness.
• Highlights previously unidentified threats to trustworthiness such as generation of toxic outputs.
• GPT-4 surpasses GPT-3.5 in standard benchmarks but shows higher vulnerability to misleading prompts.

Opinion: The critical evaluation of trustworthiness in AI models like GPT-4 is paramount for their safe application in sensitive domains like healthcare and finance. Understanding these vulnerabilities allows developers to bolster AI defenses and paves the way for creating more robust and reliable AI systems.