通过记忆重要性与遗忘的心理模型增强长期RAG聊天机器人
Enhancing Long-term RAG Chatbots with Psychological Models of Memory Importance and Forgetting
AI总结：
LUFY通过优先保留情绪唤醒记忆并遗忘大部分对话内容，提升长期对话体验和聊天机器人性能。
摘要：
尽管检索增强生成（RAG）在增强长期对话方面展现出潜力，但随着对话的进行，记忆负载的增加会降低检索准确性。基于心理学见解，我们提出LUFY，一种简单但有效的方法，专注于情绪唤醒的记忆并保留少于10%的对话内容。在用户实验中，参与者与三种类型的RAG聊天机器人互动，每种机器人在4次会话中各进行2小时的对话，标志着迄今为止对聊天机器人长期能力的最全面评估——比现有任何基准测试长超过四倍。结果表明，优先考虑唤醒记忆并遗忘大部分对话内容显著提升了用户体验。本研究推动了长期对话的前沿，并突显了遗忘对话中不重要部分的重要性。代码和数据集：https://github.com/ryuichi-sumida/LUFY，Huggingface数据集：https://huggingface.co/datasets/RuiSumida/LUFY
英文摘要：
While Retrieval-Augmented Generation (RAG) has shown promise in enhancing long-term conversations, the increasing memory load as conversations progress degrades retrieval accuracy. Drawing on psychological insights, we propose LUFY, a simple yet effective method that focuses on emotionally arousing memories and retains less than 10% of the conversation. In the user experiment, participants interacted with three types of RAG chatbots, each for 2 hours over 4 sessions, marking the most extensive assessment of a chatbot's long-term capabilities to date -- more than four times longer than any existing benchmark. The results demonstrate that prioritizing arousing memories while forgetting the majority of the conversation significantly enhances user experience. This study pushes the frontier of long-term conversations and highlights the importance of forgetting unimportant parts of conversations. Code and Dataset: https://github.com/ryuichi-sumida/LUFY, Hugginface Dataset:https://huggingface.co/datasets/RuiSumida/LUFY
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。
