
Safety & Harmful Content Evaluation (LLM Evaluations or evals) refers to the systematic process of assessing large language models (LLMs) for their ability to avoid generating unsafe, offensive, or harmful outputs. This evaluation involves testing the model with various prompts to identify vulnerabilities, biases, or inappropriate responses, ensuring the language model adheres to ethical guidelines and safety standards. The goal is to minimize risks and promote responsible AI usage.

Safety & Harmful Content Evaluation (LLM Evaluations or evals) refers to the systematic process of assessing large language models (LLMs) for their ability to avoid generating unsafe, offensive, or harmful outputs. This evaluation involves testing the model with various prompts to identify vulnerabilities, biases, or inappropriate responses, ensuring the language model adheres to ethical guidelines and safety standards. The goal is to minimize risks and promote responsible AI usage.
What is Safety & Harmful Content Evaluation?
It is the process of identifying content that could cause harm or violate safety policies and deciding on actions (remove, restrict, or label) to protect users.
Which content categories are typically considered harmful?
Categories commonly flagged include violence, self-harm or encouragement, harassment or hate speech, sexual content involving minors or explicit material, dangerous or illegal activity, and misinformation that could cause harm.
What signals do evaluators look for when assessing safety?
They consider context and intent, target audience, potential harm, level of detail, whether harm is encouraged or instructional, and how well the content aligns with platform rules.
What actions are taken after a safety evaluation?
Actions may include removing or restricting access, adding warnings or age gates, tagging for review, reporting to authorities if required, and documenting the decision for future audits.