通过聚类与奖励引导解码，实现高效个性化文本生成。
CARD: Cluster-level Adaptation with Reward-guided Decoding for Personalized Text Generation
- 先聚类用户风格，再用轻量LoRA适配器提升个性化效果。
- 在LaMP和LongLaMP上性能优于或持平主流方法。
- 推理时仅用少量向量注入，适合大规模部署。
将大语言模型适配至个体用户仍面临细粒度个性化与可扩展部署之间的矛盾。我们提出CARD，一种分层框架，通过渐进式优化实现有效个性化。CARD首先根据共享风格模式对用户进行聚类，并学习簇级的LoRA适配器，从而实现稳健泛化与强低资源表现。为捕捉簇内个体差异，我们提出隐式偏好学习机制，通过对比用户自写文本与簇级生成结果，使模型无需人工标注即可推断用户特定风格偏好。推理时，CARD仅在解码阶段通过轻量级用户偏好向量和低秩logit修正注入个性化信息，保持基础模型冻结。在LaMP和LongLaMP基准上的实验表明，CARD在生成质量上达到或超越现有最优基线，同时显著提升效率与可扩展性，适用于实际个性化文本生成场景。
原文摘要 · Abstract (English)
Adapting large language models to individual users remains challenging due to the tension between fine-grained personalization and scalable deployment. We present CARD, a hierarchical framework that achieves effective personalization through progressive refinement. CARD first clusters users according to shared stylistic patterns and learns cluster-specific LoRA adapters, enabling robust generalization and strong low-resource performance. To capture individual differences within each cluster, we propose an implicit preference learning mechanism that contrasts user-authored text with cluster-level generations, allowing the model to infer user-specific style preferences without manual annotation. At inference time, CARD injects personalization exclusively at decoding via lightweight user preference vectors and low-rank logit corrections, while keeping the base model frozen. Experiments on the LaMP and LongLaMP benchmarks show that CARD achieves competitive or superior generation quality compared to state-of-the-art baselines, while significantly improving efficiency and scalability for practical personalized text generation.
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