arXiv:2606.01811v1 Announce Type: new Abstract: Measuring the diversity of creative outputs is central to evaluating post-training mode collapse, comparing decoding strategies, and quantifying creative behavior in both AI and human writing. We propose a new approach to measuring diversity using in-context learning, of which the ``Decan'' metric, $D_{Ca_n} = C \times a_n$, is the working instance we evaluate: a per-byte score read off the per-token log-probabilities of a base model $\theta$ in a \emph{single forward pass} per permutation, with no embedding model, no reference corpus, and no hum
The proliferation of generative AI models necessitates robust methods for evaluating creative output, especially concerning mode collapse and diversity.
This new metric provides a more efficient and less resource-intensive way to measure the diversity of AI-generated content, crucial for model development and comparison.
AI model developers can now assess the diversity of their models' outputs more accurately and with fewer computational resources, potentially accelerating progress in creative AI applications.
- · AI researchers
- · Generative AI companies
- · Creative content platforms
- · AI art/writing communities
- · Inefficient diversity measurement methods
- · Resource-constrained AI developers
Improved understanding and mitigation of mode collapse in generative AI models.
Faster iteration and development of more diverse and creative AI systems.
Enhanced ability to differentiate human vs. AI-generated content based on diversity characteristics.
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Read at arXiv cs.CL