Generative AI enables users to rapidly generate new content based on a variety of inputs. It uses neural networks to identify the patterns and structures within existing data to generate new and original content. And its ability to leverage different learning approaches, including unsupervised or semi-supervised learning for training has given organisations the ability to more easily and quickly leverage a large amount of unlabeled data to create foundation models. A well-known foundation model GPT-3, allows users to generate a long-form text based on a short text request. Evaluating generative AI models involves assessing their performance, quality, and relevance to the intended task.
Evaluating Generative AI models
According to Nvidia, there are 3 key requirements for a successful generative AI model. Evaluating generative AI models involves assessing their performance, quality, and relevance to the intended task.
Quality
Having high-quality generation output is key for applications that interact directly with users. A great example of such systems would be speech, a poor-quality speech would be difficult to understand. For image generation, you might use metrics like Inception Score or Frechet Inception Distance (FID).
Diversity
A good generative AI application captures minority and majority modules in its data distribution effectively without sacrificing quality. This reduces undesired biases. Metrics like diversity score or novelty can help quantify this aspect.
Speed
Interactive applications require faster generation for example real-time image editing. Low-latency and high-speed performance are essential to provide a seamless user experience.
The choice of evaluation metrics depends on the specific task and goals of the generative AI system. A combination of quantitative metrics can be used, metrics such as inception score (IS), Fréchet inception distance (FID), or precision and recall for distributions (PRD) to measure how well the generated data matches the real data distribution. They can provide objective and standardized measures of generative model performance. Another way of evaluating generative AI models is qualitative methods, which involve inspecting the generated data visually or auditorily. It involves methods such as interpolation, latent space exploration, or conditional generation to test how the generative model responds to different inputs or parameters.
