Papers by Kai Tzu-iunn Ong
PRINCIPLES: Synthetic Strategy Memory for Proactive Dialogue Agents (2025.findings-emnlp)
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Namyoung Kim, Kai Tzu-iunn Ong, Yeonjun Hwang, Minseok Kang, Iiseo Jihn, Gayoung Kim, Minju Kim, Jinyoung Yeo
| Challenge: | Existing strategies for proactive dialogue face limitations such as limited strategy coverage and preference bias in planning. |
| Approach: | They propose a synthetic strategy memory for proactive dialogue agents based on large language models . PRINCIPLES is derived through offline self-play simulations and serves as reusable knowledge that guides strategy planning during inference. |
| Outcome: | PRINCIPLES is a synthetic strategy memory for proactive dialogue agents. |
Towards Lifelong Dialogue Agents via Timeline-based Memory Management (2025.naacl-long)
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Kai Tzu-iunn Ong, Namyoung Kim, Minju Gwak, Hyungjoo Chae, Taeyoon Kwon, Yohan Jo, Seung-won Hwang, Dongha Lee, Jinyoung Yeo
| Challenge: | Existing studies focus on getting rid of outdated memories to improve retrieval quality, but we argue that such memories provide rich, important contextual cues for response generation (RG). |
| Approach: | They propose a framework for LLM-based lifelong dialogue agents that discards memory removal and manages large-scale memories by linking them based on their temporal and cause-effect relation. |
| Outcome: | The proposed framework augments RG with memory timelines based on evolution or causality of relevant past events. |