arXiv:2602.00742v2 Announce Type: replace Abstract: User modeling characterizes individuals through their preferences and behavioral patterns to enable personalized simulation and generation with Large Language Models (LLMs) in contemporary approaches. However, existing methods, whether prompt-based or training-based methods, face challenges in balancing personalization quality against computational and data efficiency. We propose a novel framework CURP, which employs a bidirectional user encoder and a discrete prototype codebook to extract multi-dimensional user traits. This design enables pl
The proliferation of LLMs and the increasing demand for personalized interactions are driving innovation in user modeling, seeking more efficient and effective methods.
This development offers a more efficient and personalized approach to user modeling for LLMs, enhancing their utility in various applications without sacrificing computational resources.
The method of representing continuous user data for personalized generation with LLMs changes, moving towards more compact and multi-dimensional encoding.
- · AI developers
- · Personalized content platforms
- · E-commerce
- · Customer service technologies
- · Inefficient prompt-based personalization methods
- · Computationally intensive training-based methods
More accurate and resource-efficient personalized experiences using LLMs become possible.
The ability to scale personalized AI applications increases significantly across various industries.
This could lead to a broader adoption of AI-driven personalized services, potentially influencing user expectations for digital interactions.
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