基于参数化记忆注入的个性化大语言模型响应生成
Personalized LLM Response Generation with Parameterized Memory Injection
AI总结：
针对现有记忆增强式LLM个性化方法无法感知细粒度信息的问题，提出结合参数高效微调与贝叶斯优化搜索策略的记忆注入式个性化方法MiLP，以提升LLM响应的个性化效果。
摘要：
大语言模型（LLM）在理解和生成自然语言方面已展现出卓越能力。另一方面，个性化LLM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，即向LLM输入预存的用户特定知识，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。本研究提出一种新颖的记忆注入方法MiLP（记忆注入式大语言模型个性化），该方法采用参数高效微调（PEFT）并结合贝叶斯优化搜索策略来实现LLM个性化。
英文摘要：
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}).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
