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October 20, 2025Open Access

PerQ: Efficient Evaluation of Multilingual Text Personalization Quality

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Authors

DMDominik MackoAPAndrew B. Pulver

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Overview

Case study compares generation capabilities of language models, demonstrating the need for effective evaluation metrics.

Key Points

  • The proposed method, PerQ, evaluates the personalization quality of multilingual texts efficiently without high resource costs.
  • Evaluation reveals that using PerQ can effectively reduce biases compared to relying solely on large language models.
  • The case study indicates that resource efficiency is achieved by employing PerQ in assessing language model outputs.
  • Prior reliance on multiple models for quality assessment often led to unnecessary costs, which PerQ mitigates.

Cite This Study

Macko et al. (2025) studied this question.

synapsesocial.com/papers/68f5fcd68d54a28a75cf1f06https://doi.org/10.48550/arxiv.2509.25903
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1PerSEval: Assessing Personalization in Text Summarizers2024
  2. 2QuRating: Selecting High-Quality Data for Training Language Models2024 · 1 citations
  3. 3Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph2024 · 2 citations
  4. 4Reference-Less Evaluation of Machine Translation: Navigating Through the Resource-Scarce Scenarios2025
  5. 5Text Quality-Based Pruning for Efficient Training of Language Models2024