PRIME: Large Language Model Personalization with Cognitive Dual-Memory and Personalized Thought Process
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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914058629808128 |
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| author | Zhang, Xinliang Frederick Beauchamp, Nick Wang, Lu |
| author_facet | Zhang, Xinliang Frederick Beauchamp, Nick Wang, Lu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04607 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PRIME: Large Language Model Personalization with Cognitive Dual-Memory and Personalized Thought Process Zhang, Xinliang Frederick Beauchamp, Nick Wang, Lu Computation and Language Artificial Intelligence 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. |
| title | PRIME: Large Language Model Personalization with Cognitive Dual-Memory and Personalized Thought Process |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2507.04607 |