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
@article{arxiv.2409.12524,
title = {Enhancing Long-term RAG Chatbots with Psychological Models of Memory Importance and Forgetting},
author = {Ryuichi Sumida and Koji Inoue and Tatsuya Kawahara},
journal= {arXiv preprint arXiv:2409.12524},
year = {2025}
}
Comments
37 pages, accepted and published in Dialogue & Discourse 16(2) (2025)