PerQ: Efficient Evaluation of Multilingual Text Personalization Quality

Dominik Macko; Andrew Pulver

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arXiv · 2509.25903

PerQ: Efficient Evaluation of Multilingual Text Personalization Quality

Dominik Macko·Andrew Pulver

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Abstract

Since no metrics are available to evaluate specific aspects of a text, such as its personalization quality, the researchers often rely solely on large language models to meta-evaluate such texts. Due to internal biases of individual language models, it is recommended to use multiple of them for combined evaluation, which directly increases costs of such meta-evaluation. In this paper, a computationally efficient method for evaluation of personalization quality of a given text (generated by a language model) is introduced, called PerQ. A case study of comparison of generation capabilities of large and small language models shows the usability of the proposed metric in research, effectively reducing the waste of resources.

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Dominik Macko, Andrew Pulver. 2025-09-30. PerQ: Efficient Evaluation of Multilingual Text Personalization Quality. https://arxiv.org/abs/2509.25903

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