Large Language Models (LLMs), such as GPT3.5, have exhibited remarkable proficiency in comprehending and generating natural language. However, their unpersonalized generation paradigm may result in suboptimal user-specific outcomes. Typically, users converse differently based on their knowledge and preferences. This necessitates the task of enhancing user-oriented LLM which remains unexplored. While one can fully train an LLM for this objective, the resource consumption is unaffordable. Prior research has explored memory-based methods to store and retrieve knowledge to enhance generation without retraining for new queries. However, we contend that a mere memory module is inadequate to comprehend a user's preference, and fully training an LLM can be excessively costly. In this study, we propose a novel computational bionic memory mechanism, equipped with a parameter-efficient fine-tuning schema, to personalize LLMs. Our extensive experimental results demonstrate the effectiveness and superiority of the proposed approach. To encourage further research into this area, we are releasing a new conversation dataset generated entirely by LLM based on an open-source medical corpus, as well as our implementation code.

翻译：大语言模型（如GPT3.5）在自然语言理解与生成方面展现出卓越能力。然而，其非个性化生成范式可能导致用户特定场景下的次优结果。通常情况下，用户基于自身知识与偏好采用不同交流方式，这要求我们开展尚未充分探索的用户导向型大语言模型增强任务。虽然可以通过全量训练实现该目标，但资源消耗难以承受。现有研究探索了基于存储的方法来存储和检索知识，从而在不重新训练的情况下增强新查询的生成能力。但我们认为，单纯依赖存储模块无法充分理解用户偏好，而全量训练大语言模型的代价过高。本研究提出一种新颖的计算仿生记忆机制，配备参数高效微调方案，实现大语言模型的个性化定制。大量实验结果表明所提方法具有有效性与优越性。为促进该领域的深入研究，我们基于开源医学语料库发布了完全由大语言模型生成的新型对话数据集及实现代码。