PRIME：基于认知双记忆与个性化思维过程的大型语言模型个性化
PRIME: Large Language Model Personalization with Cognitive Dual-Memory and Personalized Thought Process
AI总结：
该研究提出融合认知双记忆与个性化慢思考的 PRIME 框架，并构建基于 Reddit CMV 的长上下文个性化数据集，实验证明其可捕捉动态用户偏好。
摘要：
大型语言模型（LLM）个性化旨在使模型输出与个人独特的偏好和观点保持一致。尽管近期研究已经实现了多种个性化方法，但仍缺乏一个能够系统理解有效个性化驱动因素的统一理论框架。在本研究中，我们将成熟的认知双记忆模型整合到 LLM 个性化中，将情景记忆对应于历史用户交互，并将语义记忆对应于长期、不断演变的用户信念。具体而言，我们系统研究了记忆实例化，并引入了一个采用情景记忆和语义记忆机制的统一框架 PRIME。我们还受慢思考策略启发，以一种新颖的个性化思维能力增强 PRIME。此外，鉴于缺乏合适的基准，我们利用 Reddit 的 Change My View（CMV）引入了一个数据集，专门用于评估长上下文个性化。大量实验验证了 PRIME 在长上下文和短上下文场景中的有效性。进一步分析证实，PRIME 能够有效捕捉动态个性化，而不仅仅是流行度偏差。
英文摘要：
Large language model (LLM) personalization aims to align model outputs with individuals' unique preferences and opinions. While recent efforts have implemented various personalization methods, a unified theoretical framework that can systematically understand the drivers of effective personalization is still lacking. In this work, we integrate the well-established cognitive dual-memory model into LLM personalization, by mirroring episodic memory to historical user engagements and semantic memory to long-term, evolving user beliefs. Specifically, we systematically investigate memory instantiations and introduce a unified framework, PRIME, using episodic and semantic memory mechanisms. We further augment PRIME with a novel personalized thinking capability inspired by the slow thinking strategy. Moreover, recognizing the absence of suitable benchmarks, we introduce a dataset using Change My View (CMV) from Reddit, specifically designed to evaluate long-context personalization. Extensive experiments validate PRIME's effectiveness across both long- and short-context scenarios. Further analysis confirms that PRIME effectively captures dynamic personalization beyond mere popularity biases.
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