Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Efforts
<|reference_start|>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), 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.<|reference_end|>
@article{tan2024personalized,
    title={Personalized Pieces: Efficient Personalized Large Language Models
  through Collaborative Efforts},
    author={Zhaoxuan Tan, Zheyuan Liu, Meng Jiang},
    journal={arXiv preprint arXiv:2406.10471},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10471},
    primaryClass={cs.CL}
}