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cs.CL2026
Language Model Maps for Prompt-Response Distributions via Log-Likelihood Vectors
Momose Oyama, Yusuke Takase, Hidetoshi Shimodaira
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…
cs.CL2026
Domain Mixture Design via Log-Likelihood Differences for Aligning Language Models with a Target Model
Ryo Kishino, Riku Shiomi, Hiroaki Yamagiwa +2
Instead of directly distilling a language model, this study addresses the problem of aligning a base model with a target model in distribution by designing the domain mixture of tr…