PEFT-U: Parameter-Efficient Fine-Tuning for User Personalization

• URL: http://arxiv.org/abs/2407.18078v1
• Date: Thu, 25 Jul 2024 14:36:18 GMT
• Title: PEFT-U: Parameter-Efficient Fine-Tuning for User Personalization
• Authors: Christopher Clarke, Yuzhao Heng, Lingjia Tang, Jason Mars,
• Abstract summary: We introduce the PEFT-U Benchmark: a new dataset for building and evaluating NLP models for user personalization. We explore the challenge of efficiently personalizing LLMs to accommodate user-specific preferences in the context of diverse user-centered tasks.
• Score: 9.594958534074074
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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