{"ID":2864248,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.23767","arxiv_id":"2509.23767","title":"From Personal to Collective: On the Role of Local and Global Memory in LLM Personalization","abstract":"Large language model (LLM) personalization aims to tailor model behavior to individual users based on their historical interactions. However, its effectiveness is often hindered by two key challenges: the \\textit{cold-start problem}, where users with limited history provide insufficient context for accurate personalization, and the \\textit{biasing problem}, where users with abundant but skewed history cause the model to overfit to narrow preferences. We identify both issues as symptoms of a common underlying limitation, i.e., the inability to model collective knowledge across users. To address this, we propose a local-global memory framework (LoGo) that combines the personalized local memory with a collective global memory that captures shared interests across the population. To reconcile discrepancies between these two memory sources, we introduce a mediator module designed to resolve conflicts between local and global signals. Extensive experiments on multiple benchmarks demonstrate that LoGo consistently improves personalization quality by both warming up cold-start users and mitigating biased predictions. These results highlight the importance of incorporating collective knowledge to enhance LLM personalization.","short_abstract":"Large language model (LLM) personalization aims to tailor model behavior to individual users based on their historical interactions. However, its effectiveness is often hindered by two key challenges: the \\textit{cold-start problem}, where users with limited history provide insufficient context for accurate personaliza...","url_abs":"https://arxiv.org/abs/2509.23767","url_pdf":"https://arxiv.org/pdf/2509.23767v1","authors":"[\"Zehong Wang\",\"Junlin Wu\",\"ZHaoxuan Tan\",\"Bolian Li\",\"Xianrui Zhong\",\"Zheli Liu\",\"Qingkai Zeng\"]","published":"2025-09-28T09:32:18Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}