CARD: Cluster-level Adaptation with Reward-guided Decoding for Personalized Text Generation
Summary
CARD proposes a hierarchical framework for personalized text generation that clusters users and uses reward-guided decoding, demonstrating improved quality and efficiency on LaMP benchmarks.
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Paper page - CARD: Cluster-level Adaptation with Reward-guided Decoding for Personalized Text Generation
Source: https://huggingface.co/papers/2601.06352 Published on Sep 20
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Submitted byhttps://huggingface.co/hulehule
JWon Sep 28
Abstract
Adaptinglargelanguagemodelstoindividualusersremainschallengingduetothetensionbetweenfine-grainedpersonalizationandscalabledeployment.WepresentCARD,ahierarchicalframeworkthatachieveseffectivepersonalizationthroughprogressiverefinement.CARDfirstclustersusersaccordingtosharedstylisticpatternsandlearnsgroup-specificLoRAadapters,enablingrobustgeneralizationandstronglow-resourceperformance.Tocaptureindividualdifferenceswithineachcluster,weproposeanimplicitpreferencelearningmechanismthatcontrastsuser-authoredtextwithcluster-levelgenerations,allowingthemodeltoinferuser-specificstylepreferenceswithoutmanualannotation.Atinferencetime,CARDinjectspersonalizationexclusivelyatdecodingvialightweightuserpreferencevectorsandlow-ranklogitcorrections,whilekeepingthebasemodelfrozen.ExperimentsontheLaMPandLongLaMPbenchmarksshowthatCARDachievessuperiorgenerationqualitycomparedtobaselines,whilesignificantlyimprovingefficiencyandscalabilityforpracticalpersonalizedtextgeneration.
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