PEFT-U: Parameter-Efficient Fine-Tuning for User Personalization
<|reference_start|>PEFT-U: Parameter-Efficient Fine-Tuning for User Personalization: The recent emergence of Large Language Models (LLMs) has heralded a new era of human-AI interaction. These sophisticated models, exemplified by Chat-GPT and its successors, have exhibited remarkable capabilities in language understanding. However, as these LLMs have undergone exponential growth, a crucial dimension that remains understudied is the personalization of these models. Large foundation models such as GPT-3 etc. focus on creating a universal model that serves a broad range of tasks and users. This approach emphasizes the model's generalization capabilities, treating users as a collective rather than as distinct individuals. While practical for many common applications, this one-size-fits-all approach often fails to address the rich tapestry of human diversity and individual needs. To explore this issue we introduce the PEFT-U Benchmark: a new dataset for building and evaluating NLP models for user personalization. \datasetname{} consists of a series of user-centered tasks containing diverse and individualized expressions where the preferences of users can potentially differ for the same input. Using PEFT-U, we explore the challenge of efficiently personalizing LLMs to accommodate user-specific preferences in the context of diverse user-centered tasks.<|reference_end|>
@article{clarke2024peft-u:,
    title={PEFT-U: Parameter-Efficient Fine-Tuning for User Personalization},
    author={Christopher Clarke, Yuzhao Heng, Lingjia Tang, Jason Mars},
    journal={arXiv preprint arXiv:2407.18078},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18078},
    primaryClass={cs.CL cs.AI}
}