PARL formulates personalized LLM evaluation as a learning problem that induces preference-aware rubrics from raw user histories via discriminative RL and self-validation.
Automated evaluation of personalized text generation using large language models
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Preference-Aware Rubric Learning for Personalized Evaluation
PARL formulates personalized LLM evaluation as a learning problem that induces preference-aware rubrics from raw user histories via discriminative RL and self-validation.