具有演化条件记忆的个性化大语言模型助手
Personalized Large Language Model Assistant with Evolving Conditional Memory
AI总结：
本文提出一个即插即用的框架，通过演化条件记忆机制保存用户历史对话知识，实现个性化大语言模型助手，并构建首个评估基准验证了其有效性。
摘要：
随着大语言模型的快速发展，像ChatGPT这样的AI助手已日益融入人们的工作和生活，但在个性化服务方面存在局限。在本文中，我们提出了一个即插即用的框架，能够通过演化条件记忆促进个性化大语言模型助手的发展。该个性化助手专注于智能地保存从与用户的历史对话中获取的知识和经验，这可用于未来更好地符合用户偏好的定制化响应。通常，助手从对话中生成一组记录，将其存储在记忆库中，并检索相关记忆以提高响应质量。对于关键的记忆设计，我们探索了构建记忆的不同方式，并提出了一种名为条件记忆的新记忆机制。我们还研究了生成过程中记忆的检索和使用。我们构建了首个基准，从三个方面评估个性化助手的能力。实验结果证明了我们方法的有效性。
英文摘要：
With the rapid development of large language models, AI assistants like ChatGPT have become increasingly integrated into people's works and lives but are limited in personalized services. In this paper, we present a plug-and-play framework that could facilitate personalized large language model assistants with evolving conditional memory. The personalized assistant focuses on intelligently preserving the knowledge and experience from the history dialogue with the user, which can be applied to future tailored responses that better align with the user's preferences. Generally, the assistant generates a set of records from the dialogue dialogue, stores them in a memory bank, and retrieves related memory to improve the quality of the response. For the crucial memory design, we explore different ways of constructing the memory and propose a new memorizing mechanism named conditional memory. We also investigate the retrieval and usage of memory in the generation process. We build the first benchmark to evaluate personalized assistants' ability from three aspects. The experimental results illustrate the effectiveness of our method.
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。
