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PrefReward introduces a novel framework for personalized text generation by explicitly modeling user stylistic tendencies through a structured preference matrix integrated into the decoding process. This approach addresses limitations in current implicit personalization methods, offering improved interpretability of user-specific preferences and better handling of long-context dependencies within Large Language Models. The method demonstrates superior performance over standard baselines on the LongLaMP dataset while maintaining generation quality.
PrefReward: Learning User Preference Matrix for Personalized Text Generation
arXiv:2607.21067v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable ability in generating personalized content by leveraging user histories and contextual cues. However, most existing personalization approaches rely on implicit representations within model parameters, making it difficult to interpret user-specific preferences or effectively handle long-context dependencies. To address these challenges, we propose PrefReward, a novel preference-aware generative framework that explicitly models user styles through a structured preference matrix and integrates it into the decoding process as a reward signal. PrefReward consists of two stages: (1) extracting a user-specific preference matrix that summarizes individual stylistic tendencies, and (2) using the matrix to guide generation via a KL-divergence-based reward function. Experiments on the LongLaMP dataset show that PrefReward outperforms non-personalized and retrieval-based baselines in both generation quality and personalization interpretability.
Abstract:Large Language Models (LLMs) have demonstrated remarkable ability in generating personalized content by leveraging user histories and contextual cues. However, most existing personalization approaches rely on implicit representations within model parameters, making it difficult to interpret user-specific preferences or effectively handle long-context dependencies. To address these challenges, we propose PrefReward, a novel preference-aware generative framework that explicitly models user styles through a structured preference matrix and integrates it into the decoding process as a reward signal. PrefReward consists of two stages: (1) extracting a user-specific preference matrix that summarizes individual stylistic tendencies, and (2) using the matrix to guide generation via a KL-divergence-based reward function. Experiments on the LongLaMP dataset show that PrefReward outperforms non-personalized and retrieval-based baselines in both generation quality and personalization interpretability.
Computation and Language (cs.CL)
arXiv:2607.21067 [cs.CL]
(or arXiv:2607.21067v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.21067 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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