PRIME: Large Language Model Personalization with Cognitive Memory and Thought Processes

• URL: http://arxiv.org/abs/2507.04607v2
• Date: Mon, 14 Jul 2025 05:54:45 GMT
• Title: PRIME: Large Language Model Personalization with Cognitive Memory and Thought Processes
• Authors: Xinliang Frederick Zhang, Nick Beauchamp, Lu Wang,
• Abstract summary: Large language model (LLM) personalization aims to align model outputs with individuals' unique preferences and opinions.<n>We introduce a unified framework, PRIME, using episodic and semantic memory mechanisms.<n>Experiments validate PRIME's effectiveness across both long- and short-context scenarios.
• Score: 6.631626634132574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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