Mapping 1,000+ Language Models via the Log-Likelihood Vector

ACL ARR 2025 February Submission4064 Authors

Abstract: 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 theoretical basis: when treated as model coordinates, their squared Euclidean distance approximates the Kullback–Leibler divergence of text-generation probabilities. Our method is highly scalable, with computational cost growing linearly in both the number of models and text samples, and is easy to implement as the required features are derived from cross-entropy loss. Applying this method to over 1,000 language models, we constructed a ``model map,'' providing a new perspective on large-scale model analysis.

Paper Type: Long

Research Area: Language Modeling

Research Area Keywords: scaling

Contribution Types: Data analysis, Theory

Languages Studied: English

Submission Number: 4064