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 corresponding conditional distributions. Experiments on a large collection of publicly available language models show that the maps capture meaningful global structure, including relationships to model attributes and task performance. The representation also captures systematic shifts induced by prompt modifications and their approximate additive compositionality; we use the vectors to predict ...
| Authors | Momose Oyama, Yusuke Takase, Hidetoshi Shimodaira |
|---|---|
| Primary category | cs.CL |
| Published | 19 March 2026 |
| Last updated at source | 2 September 2026 |
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