PRIME: Language Model Personalization with Cognitive Memory and Thought Processes

ACL ARR 2025 May Submission7234 Authors

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 CMV dataset 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.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: LLM personalization

Contribution Types: NLP engineering experiment, Data resources

Languages Studied: English

Submission Number: 7234