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Found 4 papers, 0 papers with code
Exploring Precision and Recall to assess the quality and diversity of LLMs
We introduce a novel evaluation framework for Large Language Models (LLMs) such as \textsc{Llama-2} and \textsc{Mistral}, focusing on importing Precision and Recall metrics from image generation to text generation.
Optimal Budgeted Rejection Sampling for Generative Models
Rejection sampling methods have recently been proposed to improve the performance of discriminator-based generative models.
Ranked #16 on
Image Generation
on CelebA 64x64
Training Normalizing Flows with the Precision-Recall Divergence
Generative models can have distinct mode of failures like mode dropping and low quality samples, which cannot be captured by a single scalar metric.
On the expressivity of bi-Lipschitz normalizing flows
An invertible function is bi-Lipschitz if both the function and its inverse have bounded Lipschitz constants.
