Enhancing Long-term RAG Chatbots with Psychological Models of Memory Importance and Forgetting
On the map: Retrieval-augmented generation → Conversational and speech RAG
Abstract (arXiv)
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
Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Placed in Conversational and speech RAG, one of 15 directions in Retrieval-augmented generation. Builds on 18 of its 48 references that are in the map. First posted on arXiv 19 Sep 2024.
Builds on
18 of 48 references.
References · 18
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6 more
- 2022EmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysiscited by 75
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Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2024Evaluating Very Long-Term Conversational Memory of LLM Agents
- 2024LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory
- 2026ES-MemEval: Benchmarking Conversational Agents on Personalized Long-Term Emotional Support
- 2025REALTALK: A 21-Day Real-World Dataset for Long-Term Conversation
- 2025MMAG: Mixed Memory-Augmented Generation for Large Language Models Applications
- 2024Ever-Evolving Memory by Blending and Refining the Past
- 2024Adaptive Retrieval-Augmented Generation for Conversational Systems
- 2025Convomem Benchmark: Why Your First 150 Conversations Don't Need RAG