Conditional Vendi Score: Prompt-Aware Diversity Evaluation for Generative AI Models and LLMs
arXiv:2411.02817v2 Announce Type: replace Abstract: Generative models guided by text prompts are widely evaluated for fidelity and prompt alignment, yet their ability to produce outputs remains underexplored. Existing diversity metrics such as Vendi and RKE, which are based on the von Neumann and R\'enyi entropies of kernel matrices, were developed for unconditional models and cannot distinguish prompt-induced from model-induced variability. We address this gap by introducing \textit{Conditional-Vendi} and \textit{Conditional-RKE}, diversity measures derived from the conditional entropy of pos
The rapid advancement and widespread adoption of generative AI, particularly LLMs, necessitates more sophisticated evaluation metrics beyond initial fidelity and alignment measures.
Improved diversity evaluation is crucial for the reliable development and deployment of generative AI models, ensuring outputs are not only accurate but also varied and innovative.
The introduction of Conditional Vendi and Conditional RKE allows for differentiating model-induced variability from prompt-induced variability, providing a more nuanced understanding of generative AI capabilities.
- · AI model developers
- · AI researchers
- · AI evaluation platforms
- · Generative AI models with poor diversity
- · Evaluation methods relying solely on existing metrics
Generative AI models will be evaluated more comprehensively for output diversity, leading to more robust model development.
Improved diversity metrics could accelerate the development of more creative and less biased generative AI applications across various industries.
A deeper understanding of prompt-induced variability might lead to entirely new paradigms for prompt engineering and human-AI collaboration.
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Read at arXiv cs.LG