Toward Multi-Session Personalized Conversation: A Large-Scale Dataset and Hierarchical Tree Framework for Implicit Reasoning (2025.emnlp-main)
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| Challenge: | Existing long-term open-domain dialogue datasets lack complex, real-world personalization and fail to capture implicit reasoning. |
| Approach: | They propose a large-scale long-term dataset with 2,500 examples containing approximately 100 conversation sessions to study implicit reasoning in personalized dialogues. |
| Outcome: | The proposed model improves the ability of LLMs to reason over long-term conversations with implicit contextual dependencies. |
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