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}).

翻译：大语言模型在理解与生成自然语言方面展现出卓越能力。另一方面，个性化大语言模型响应生成有望在医疗等关键领域为个人带来显著益处。现有研究探索了记忆增强方法，通过预存用户特定知识提示大语言模型，以针对新查询生成个性化响应。我们认为此类范式无法感知细粒度信息。本研究提出一种基于参数高效微调（PEFT）的新型记忆注入方法，结合贝叶斯优化搜索策略，实现大语言模型个性化（MiLP）。