Generation-Based and Emotion-Reflected Memory Update: Creating the KEEM Dataset for Better Long-Term Conversation

• URL: http://arxiv.org/abs/2601.05548v1
• Date: Fri, 09 Jan 2026 05:59:36 GMT
• Title: Generation-Based and Emotion-Reflected Memory Update: Creating the KEEM Dataset for Better Long-Term Conversation
• Authors: Jeonghyun Kang, Hongjin Kim, Harksoo Kim,
• Abstract summary: We introduce a novel generation-based dataset designed to enhance memory updates in long-term conversational systems.<n>By seamlessly updating a system's memory with both emotional and essential data, our approach promotes deeper empathy and enhances the system's ability to respond meaningfully in open-domain conversations.
• Score: 12.913297153664972
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we introduce the Keep Emotional and Essential Memory (KEEM) dataset, a novel generation-based dataset designed to enhance memory updates in long-term conversational systems. Unlike existing approaches that rely on simple accumulation or operation-based methods, which often result in information conflicts and difficulties in accurately tracking a user's current state, KEEM dynamically generates integrative memories. This process not only preserves essential factual information but also incorporates emotional context and causal relationships, enabling a more nuanced understanding of user interactions. By seamlessly updating a system's memory with both emotional and essential data, our approach promotes deeper empathy and enhances the system's ability to respond meaningfully in open-domain conversations.

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