# PerQ: Efficient Evaluation of Multilingual Text Personalization Quality

arXiv: https://arxiv.org/abs/2509.25903 · HTML: https://mlsanity.com/paper/2509.25903/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Builds on (most cited first, 7 of 7)

- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [LaMP: When Large Language Models Meet Personalization](https://mlsanity.com/paper/2304.11406/) (2023)
- [ExPerT: Effective and Explainable Evaluation of Personalized Long-Form Text Generation](https://mlsanity.com/paper/2501.14956/) (2025)
- [Evaluation of LLM Vulnerabilities to Being Misused for Personalized Disinformation Generation](https://mlsanity.com/paper/2412.13666/) (2024)
- [METAL: Towards Multilingual Meta-Evaluation](https://mlsanity.com/paper/2404.01667/) (2024)
- [Exploring the Multilingual NLG Evaluation Abilities of LLM-Based Evaluators](https://mlsanity.com/paper/2503.04360/) (2025)
- [PREF: Reference-Free Evaluation of Personalised Text Generation in LLMs](https://mlsanity.com/paper/2508.10028/) (2025)

## Abstract (arXiv)

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.