PerSPL: Personalized Semi-supervised Preference Learning
We propose PerSPL (Personalized Semi-supervised Preference Learning), a backbone-agnostic framework that uses pair-mean score centering to stabilize this cross-prompt score coordinate and iteratively turns unlabeled responses into user-specific training signals.
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Key points
- Personalized reward models require user-specific preference comparisons, but collecting such feedback is costly and provides only limited supervision for each user.
- Against seven representative baselines across four benchmarks---PersonalLLM, HelpSteer2, Reddit TLDR, and Chatbot Arena---PerSPL achieves the highest reported mean accuracy among the evaluated methods for both seen and unseen users.
- The two latter benchmarks provide real-user preference comparisons in summarization and open-domain chatbot interaction, respectively.
- These results indicate that unlabeled responses can provide additional supervision for personalized reward modeling when per-user preference labels are scarce.
Sources (1)
- [1]PerSPL: Personalized Semi-supervised Preference LearningarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 05:21 AM
We propose PerSPL (Personalized Semi-supervised Preference Learning), a backbone-agnostic framework that uses pair-mean score centering to stabilize this cross-prompt score coordinate and iteratively turns unlabeled responses into user-specific training signals.
Personalized reward models require user-specific preference comparisons, but collecting such feedback is costly and provides only limited supervision for each user.
Extractive summary: sentences quoted from the sources.
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