Grounding an LLM in verbalized transaction histories via Person–Environment prompting, continued pre-training, SFT, and GRPO yields stronger retail decision simulation than frontier models, with partial cross-domain transfer.
CURP: Codebook-based Continuous User Representation for Personalized Generation with LLMs
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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 plug-and-play personalization with a small number of trainable parameters (about 20M parameters, about 0.2\% of the total model size). Through extensive experiments on variant generation tasks, we show that CURP achieves superior performance and generalization compared to strong baselines, while offering better interpretability and scalability. The code are available at https://github.com/RaidonWong/CURP_code
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cs.AI 1years
2026 1verdicts
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Large Behavior Model: A Promptable Digital Twin of the Retail Customer
Grounding an LLM in verbalized transaction histories via Person–Environment prompting, continued pre-training, SFT, and GRPO yields stronger retail decision simulation than frontier models, with partial cross-domain transfer.