RPM：面向黑盒大语言模型的推理层面个性化
RPM: Reasoning-Level Personalization for Black-Box Large Language Models
AI总结：
RPM通过构建用户行为结构模型，实现黑盒大语言模型的推理层面个性化，提升个性化性能与可解释性。
摘要：
尽管黑盒大语言模型被广泛部署，但它们生成的输出过于通用，忽略了个体用户偏好。当前的个性化方法本质上仅限于响应层面的个性化；它们只匹配最终输出，无法建模连接用户行为到响应的底层推理过程。为了解决这个问题，本文引入了推理层面的个性化作为一种新范式，并提出了RPM，这是首个系统框架，能够自动从原始行为数据中发现用户特定的推理结构，以指导模型的个性化推理。RPM构建了一个基于响应影响特征和统计因素的用户行为结构模型，以创建个性化推理路径，并通过基于特征的检索机制检索有益的例子，以指导推理。在四个多样化任务上的广泛实验表明，RPM在一致地优于现有响应层面方法的同时，也同时提升了个性化性能和可解释性，为黑盒LLM的个性化提供了有前途的方向。
英文摘要：
While black-box large language models are widely deployed, they produce generic outputs that overlook individual user preferences. Current personalization methods are fundamentally limited to response-level personalization; they only match final outputs, failing to model the underlying reasoning that connects user behavior to responses. To address this, this work introduces reasoning-level personalization as a new paradigm and proposes RPM, the first systematic framework that automatically discovers user-specific reasoning structures from raw behavioral data to guide the model's personalized inference. RPM constructs a structured model of user behavior-built from response-influential features and statistical factors-to create personalized reasoning paths and retrieve beneficial examples for guiding inference through a feature-based retrieval mechanism. Extensive experiments across four diverse tasks demonstrate that RPM consistently outperforms existing response-level methods while simultaneously enhancing both personalization performance and interpretability, providing a promising direction for black-box LLM personalization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
