  Personalized large language models (LLMs) aim to tailor interactions,
content, and recommendations to individual user preferences. While
parameter-efficient fine-tuning (PEFT) methods excel in performance and
generalization, they are costly and limit communal benefits when used
individually. To this end, we introduce Personalized Pieces (Per-Pcs), a
framework that allows users to safely share and assemble personalized PEFT
efficiently with collaborative efforts. Per-Pcs involves selecting sharers,
breaking their PEFT into pieces, and training gates for each piece. These
pieces are added to a pool, from which target users can select and assemble
personalized PEFT using their history data. This approach preserves privacy and
enables fine-grained user modeling without excessive storage and computation
demands. Experimental results show Per-Pcs outperforms non-personalized and
PEFT retrieval baselines, offering performance comparable to OPPU with
significantly lower resource use across six tasks. Further analysis highlights
Per-Pcs's robustness concerning sharer count and selection strategy, pieces
sharing ratio, and scalability in computation time and storage space. Per-Pcs's
modularity promotes safe sharing, making LLM personalization more efficient,
effective, and widely accessible through collaborative efforts.
