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Language Model Maps for Prompt-Response Distributions via Log-Likelihood Vectors
We propose a method that represents language models by log-likelihood vectors over prompt-response pairs and constructs model maps for comparing their conditional distributions. In this space, squared Euclidean distances between models are approximately proportional to the KL divergence between the
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