Generative models must be resilient to adversarial attacks and ensure the safety of their outputs.

  • Adversarial Robustness: the model's ability to resist adversarial inputs designed to exploit vulnerabilities (e.g., generating inappropriate content from ambiguous or misleading prompts).
  • Safety Metrics: Evaluating the AI for potentially harmful outputs such as hate speech, misinformation, or content that violates ethical guidelines. This is crucial in high-risk applications like news generation, social media content, and conversational AI.
  • Toxicity and Harmfulness: Measuring the tendency of the model to generate toxic, harmful, or unsafe content. Tools like Perspective API or OpenAI’s Content Moderation API are used for detecting harmful content in text generation models.

Evaluation Tools:

  • Adversarial Testing: harmful input examples to check how the model behaves (e.g., does it generate hate speech when given an offensive input prompt?).
  • Content Moderation Filters: Checking if the AI has proper safety layers to prevent harmful outputs.