Adapting large language models to individual users remains challenging because systems must capture fine-grained personal preferences while serving many users efficiently. We introduce CARD, a hierarchical personalization framework that first learns preferences shared across user groups and then captures how individual users depart from those shared patterns. CARD trains a parameter-efficient adaptation module for each group, providing a reliable prior even when a user has limited history. It then learns a compact user preference vector by contrasting user-authored responses with outputs from the corresponding group model, without requiring manual annotations. During inference, both the backbone and the group adaptation module remain frozen, while the user vector applies a low-rank correction to the token logits during decoding. Experiments on personalization benchmarks show that CARD consistently outperforms competitive baselines while requiring only a compact vector for each user.

Personalized large language models are often expected to follow explicit style instructions, yet we find that such instructions can undermine the user-specific characteristics that personalization methods aim to preserve. We call this failure mode personalization collapse: explicit style control can conflict with implicit user preferences. To address this challenge, we propose PsPLUG, a lightweight plug-in that learns a user-specific residual after accounting for the requested style. PsPLUG also allows us to tune personalization strength at inference time. Our experiments show that explicit style instructions can diminish personalization in existing methods, whereas PsPLUG better preserves user preferences while providing precise control over the balance between personalization and style adherence.

Knowledge based question answering (KBQA) is a complex task for natural language understanding. Many KBQA approaches have been proposed in recent years, and most of them are trained based on labeled reasoning path. This hinders the system’s performance as many correct reasoning paths are not labeled as ground truth, and thus they cannot be learned. In this paper, we introduce a new concept of KBQA system which can leverage multiple reasoning paths’ information and only requires labeled answer as supervision. We name it as Mutliple Reasoning Paths KBQA System (MRP-QA). We conduct experiments on several benchmark datasets containing both single-hop simple questions as well as muti-hop complex questions, including WebQuestionSP (WQSP), ComplexWebQuestion-1.1 (CWQ), and PathQuestion-Large (PQL), and demonstrate strong performance.