用双记忆机制让大模型更懂用户偏好，还能自动生成个性化思考过程。
PRIME: Large Language Model Personalization with Cognitive Dual-Memory and Personalized Thought Process
- 借鉴人类双记忆模型，区分短期互动与长期信念来建模用户
- 在长短期上下文任务中均显著提升个性化表现，超越流行度偏差
- 新提出个性化思维能力，适合需要深度理解用户的场景
大语言模型个性化旨在使模型输出符合个体独特偏好与观点。尽管已有多种个性化方法，但缺乏系统性理论框架来理解有效个性化的驱动因素。本文将成熟的认知双记忆模型引入大模型个性化，将情景记忆映射为用户历史交互，语义记忆映射为长期演变的用户信念。我们系统研究了记忆实现方式，并提出统一框架PRIME，融合情景与语义记忆机制。进一步引入受慢思考策略启发的个性化思维能力。针对现有评估基准缺失问题，我们构建基于Reddit Change My View（CMV）数据集的新数据集，专门用于评估长上下文个性化。大量实验验证了PRIME在长、短上下文场景下的有效性。深入分析表明，PRIME能有效捕捉动态个性化，而非仅依赖流行度偏差。
原文摘要 · Abstract (English)
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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