Related papers: Personalized LLM Response Generation with Parameterized Memory Injection

Personalized LLM Response Generation with Parameterized Memory Injection

URL: http://arxiv.org/abs/2404.03565v3
Date: Tue, 14 Jan 2025 15:30:50 GMT
Title: Personalized LLM Response Generation with Parameterized Memory Injection
Authors: Kai Zhang, Yejin Kim, Xiaozhong Liu,
Abstract summary: Large Language Models (LLMs) have exhibited remarkable proficiency in comprehending and generating natural language. personalized LLM response generation holds the potential to offer substantial benefits for individuals in critical areas such as medical.
Score: 17.48756504658829
License:
Abstract: Large Language Models (LLMs) have exhibited remarkable proficiency in comprehending and generating natural language. On the other hand, personalized LLM response generation holds the potential to offer substantial benefits for individuals in critical areas such as medical. Existing research has explored memory-augmented methods to prompt the LLM with pre-stored user-specific knowledge for personalized response generation in terms of new queries. We contend that such paradigm is unable to perceive fine-granularity information. In this study, we propose a novel \textbf{M}emory-\textbf{i}njected approach using parameter-efficient fine-tuning (PEFT) and along with a Bayesian Optimisation searching strategy to achieve \textbf{L}LM \textbf{P}ersonalization(\textbf{MiLP}).

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