用心理记忆模型让聊天机器人学会舍弃无意义对话，提升长期体验
Enhancing Long-term RAG Chatbots with Psychological Models of Memory Importance and Forgetting
- 基于情绪唤醒度筛选重要记忆，仅保留不足10%对话内容
- 用户连续4次交互2小时，体验显著优于传统长时聊天机器人
- 适合研究长期对话系统、记忆机制与人机交互的学者
尽管检索增强生成（RAG）在长对话中展现潜力，但随着对话推进，记忆负担加重导致检索准确率下降。借鉴心理学中的记忆重要性与遗忘机制，我们提出LUFY：一种简单有效的方法，聚焦于情绪唤醒度高的记忆，仅保留少于10%的对话内容。在用户实验中，参与者在4个会话中持续与三种RAG聊天机器人交互共计2小时，这是迄今对聊天机器人长期能力最全面的评估——远超现有基准四倍以上。结果表明，在遗忘大部分对话内容的同时优先保留情绪化记忆，能显著提升用户体验。该研究拓展了长时对话的边界，并强调遗忘非关键信息的重要性。代码与数据集：https://github.com/ryuichi-sumida/LUFY，HuggingFace数据集：https://huggingface.co/datasets/RuiSumida/LUFY。
原文摘要 · Abstract (English)
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
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