Evaluation Metrics for GenAI Systems

Evaluating generative AI models involves assessing their performance and effectiveness in creating new content, such as text, images, audio, or videos. Unlike traditional AI systems that analyze existing (training) data to classify information or predict outcomes, generative models produce novel outputs that should be coherent, creative, and relevant to the input or context.

Generative AI model evaluation is crucial today because:

• Evaluation ensures the generated content is meaningful, accurate, and meets the required standards. For example, the output must be fluent, contextually relevant, and error-free in text generation.

• Evaluation metrics help compare the GenAI system’s effectiveness, guiding developers in choosing or improving models for specific applications.

• Proper evaluation can highlight biases or harmful content in generated outputs, ensuring the ethical deployment of generative systems.

Evaluation can be conducted in two ways:

1. Automatic metrics: Quantitative measures that do not require human intervention. Usually, the evaluation metric method scores 0 (worst) to 1 (best).

2. Human evaluation: Qualitative insights gained by collecting user feedback or expert judgments. Usually, users rate the outputs on a scale of 1 (worst) to 5 (best).