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arXiv · 2602.15778

*-PLUIE: Personalisable metric with Llm Used for Improved Evaluation

Quentin Lemesle·Léane Jourdan·Daisy Munson·Pierre Alain·Jonathan Chevelu·Arnaud Delhay·Damien Lolive
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Abstract

Evaluating the quality of automatically generated text often relies on LLM-as-a-judge (LLM-judge) methods. While effective, these approaches are computationally expensive and require post-processing. To address these limitations, we build upon ParaPLUIE, a perplexity-based LLM-judge metric that estimates confidence over ``Yes/No'' answers without generating text. We introduce *-PLUIE, task specific prompting variants of ParaPLUIE and evaluate their alignment with human judgement. Our experiments show that personalised *-PLUIE achieves stronger correlations with human ratings while maintaining low computational cost.

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Quentin Lemesle, Léane Jourdan, Daisy Munson, Pierre Alain, Jonathan Chevelu, Arnaud Delhay, Damien Lolive. 2026-02-17. *-PLUIE: Personalisable metric with Llm Used for Improved Evaluation. https://arxiv.org/abs/2602.15778

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