PEBS applies Morris-James-Stein empirical-Bayes shrinkage to per-rater affine calibrators in RLHF, cutting within-user held-out RMSE by 8.58% on PRISM and 9.66% on PluriHarms versus pooled baselines.
Prlm: Learning explicit reasoning for personalized rag via contrastive reward optimization
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
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PARL formulates personalized LLM evaluation as a learning problem that induces preference-aware rubrics from raw user histories via discriminative RL and self-validation.
citing papers explorer
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PEBS: Per-rater Empirical-Bayes Shrinkage for RLHF Reward-Model Calibration
PEBS applies Morris-James-Stein empirical-Bayes shrinkage to per-rater affine calibrators in RLHF, cutting within-user held-out RMSE by 8.58% on PRISM and 9.66% on PluriHarms versus pooled baselines.
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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.