Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Efforts

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) 1 , 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.