@inproceedings{oyama-etal-2025-likelihood,
title = "Likelihood Variance as Text Importance for Resampling Texts to Map Language Models",
author = "Oyama, Momose and
Kishino, Ryo and
Yamagiwa, Hiroaki and
Shimodaira, Hidetoshi",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.502/",
doi = "10.18653/v1/2025.findings-emnlp.502",
pages = "9453--9465",
ISBN = "979-8-89176-335-7",
abstract = "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-likelihoods over a large text set, making the cost proportional to the number of texts. To reduce this cost, we propose a resampling method that selects important texts with weights proportional to the variance of log-likelihoods across models for each text. Our method significantly reduces the number of required texts while preserving the accuracy of KL divergence estimates. Experiments show that it achieves comparable performance to uniform sampling with about half as many texts, and also facilitates efficient incorporation of new models into an existing map. These results enable scalable and efficient construction of language model maps."
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<abstract>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-likelihoods over a large text set, making the cost proportional to the number of texts. To reduce this cost, we propose a resampling method that selects important texts with weights proportional to the variance of log-likelihoods across models for each text. Our method significantly reduces the number of required texts while preserving the accuracy of KL divergence estimates. Experiments show that it achieves comparable performance to uniform sampling with about half as many texts, and also facilitates efficient incorporation of new models into an existing map. These results enable scalable and efficient construction of language model maps.</abstract>
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%0 Conference Proceedings
%T Likelihood Variance as Text Importance for Resampling Texts to Map Language Models
%A Oyama, Momose
%A Kishino, Ryo
%A Yamagiwa, Hiroaki
%A Shimodaira, Hidetoshi
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F oyama-etal-2025-likelihood
%X 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-likelihoods over a large text set, making the cost proportional to the number of texts. To reduce this cost, we propose a resampling method that selects important texts with weights proportional to the variance of log-likelihoods across models for each text. Our method significantly reduces the number of required texts while preserving the accuracy of KL divergence estimates. Experiments show that it achieves comparable performance to uniform sampling with about half as many texts, and also facilitates efficient incorporation of new models into an existing map. These results enable scalable and efficient construction of language model maps.
%R 10.18653/v1/2025.findings-emnlp.502
%U https://aclanthology.org/2025.findings-emnlp.502/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.502
%P 9453-9465
Markdown (Informal)
[Likelihood Variance as Text Importance for Resampling Texts to Map Language Models](https://aclanthology.org/2025.findings-emnlp.502/) (Oyama et al., Findings 2025)
ACL