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        <title>Mapping 1,000+ Language Models via the Log-Likelihood Vector</title>
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        <namePart type="given">Hiroaki</namePart>
        <namePart type="family">Yamagiwa</namePart>
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        <namePart type="family">Takase</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Mapping 1,000+ Language Models via the Log-Likelihood Vector
%A Oyama, Momose
%A Yamagiwa, Hiroaki
%A Takase, Yusuke
%A Shimodaira, Hidetoshi
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F oyama-etal-2025-mapping
%X 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.
%R 10.18653/v1/2025.acl-long.1584
%U https://aclanthology.org/2025.acl-long.1584/
%U https://doi.org/10.18653/v1/2025.acl-long.1584
%P 32983-33038

Markdown (Informal)

[Mapping 1,000+ Language Models via the Log-Likelihood Vector](https://aclanthology.org/2025.acl-long.1584/) (Oyama et al., ACL 2025)

• Mapping 1,000+ Language Models via the Log-Likelihood Vector (Oyama et al., ACL 2025)

ACL

• Momose Oyama, Hiroaki Yamagiwa, Yusuke Takase, and Hidetoshi Shimodaira. 2025. Mapping 1,000+ Language Models via the Log-Likelihood Vector. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 32983–33038, Vienna, Austria. Association for Computational Linguistics.