Papers by Hana Kim
Commonsense-augmented Memory Construction and Management in Long-term Conversations via Context-aware Persona Refinement (2024.eacl-short)
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| Challenge: | Memorizing and utilizing speakers’ personas is a common practice for response generation in long-term conversations, yet human-authored datasets often provide uninformative persona sentences that hinder response quality. |
| Approach: | They propose a framework that leverages commonsense-based persona expansion to address such issues in long-term conversations. |
| Outcome: | The proposed framework facilitates better response generation via human-like persona refinement. |
Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Dataset (2024.findings-acl)
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Minjin Kim, Minju Kim, Hana Kim, Beong-woo Kwak, SeongKu Kang, Youngjae Yu, Jinyoung Yeo, Dongha Lee
| Challenge: | Existing datasets for conversational recommender systems lack specific user preferences and explanations for recommendations . current datasets lack specific preferences, hindering high-quality recommendations despite advances in large language models . |
| Approach: | They propose to synthesize a conversational recommendation dataset with persona- and knowledge-augmented LLM simulators to address these challenges. |
| Outcome: | The proposed dataset outperforms baselines in human and automatic evaluations. |