4 papers
Likelihood Variance as Text Importance for Resampling Texts to Map Language Models
Momose Oyama, Ryo Kishino, Hiroaki Yamagiwa +1
We address the computational cost of constructing a model map, which embeds diverse language models into a common space for comparison via KL divergence. The map relies on log-like…
Establishing a Scale for Kullback-Leibler Divergence in Language Models Across Various Settings
Ryo Kishino, Yusuke Takase, Momose Oyama +2
Log-likelihood vectors define a common space for comparing language models as probability distributions, enabling unified comparisons across heterogeneous settings. We extend this…
Mapping 1,000+ Language Models via the Log-Likelihood Vector
Momose Oyama, Hiroaki Yamagiwa, Yusuke Takase +1
To compare autoregressive language models at scale, we propose using log-likelihood vectors computed on a predefined text set as model features. This approach has a solid theoretic…
Understanding Higher-Order Correlations Among Semantic Components in Embeddings
Momose Oyama, Hiroaki Yamagiwa, Hidetoshi Shimodaira
Independent Component Analysis (ICA) offers interpretable semantic components of embeddings. While ICA theory assumes that embeddings can be linearly decomposed into independent co…