From Personal to Collective: On the Role of Local and Global Knowledge in LLM Personalization

Large language model (LLM) personalization typically relies on modeling each user in isolation, conditioning on their historical interactions to adapt model behavior. However, this user-centric formulation overlooks the collective knowledge shared across users, limiting generalization for users with sparse histories and amplifying overfitting for those with highly skewed behaviors. We argue that effective personalization requires leveraging both individual preferences and population-level patterns. To this end, we propose LoGo, a Local-Global knowledge framework that augments user-specific signals with a global knowledge encoding collective behavioral trends. LoGo models global knowledge through a temporally evolving process that captures how population-wide preferences change over time, and a community-aware structure that organizes users into coherent groups with shared interests. To balance potentially conflicting local and global signals, LoGo employs a mediator module that adaptively fuses the two knowledge sources. Experiments on five personalization benchmarks show that LoGo consistently enhances personalization quality, outperforming existing methods by improving generalization in users with limited histories and mitigating bias in users with abundant histories. These results demonstrate the central role of collective knowledge in advancing LLM personalization. Our code is publicly available at https://github.com/Zehong-Wang/LoGo .