  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}).
