Papers by Yuna Hur
EASE: Entity-Aware Sub-table Generation for Real-world Multi-table QA (2026.acl-long)
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Myunghoon Kang, Dahyun Jung, Suhyune Son, Seonmin Koo, Changwoo Chun, Daniel Rim, Haeyoung Kwon, Yuna Hur, Heuiseok Lim
| Challenge: | Table-based question answering (table QA) is a powerful tool for analyzing large language models. |
| Approach: | They propose to use noisy multi-table sets to generate sub-tables for table-based question answering. |
| Outcome: | The proposed framework efficiently filters out irrelevant information while incorporating pertinent table values. |
You Truly Understand What I Need : Intellectual and Friendly Dialog Agents grounding Persona and Knowledge (2022.findings-emnlp)
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Jungwoo Lim, Myunghoon Kang, Yuna Hur, Seung Won Jeong, Jinsung Kim, Yoonna Jang, Dongyub Lee, Hyesung Ji, DongHoon Shin, Seungryong Kim, Heuiseok Lim
| Challenge: | Existing models that ground knowledge and persona at the same time are limited, leading to hallucination and a passive way of using personas. |
| Approach: | They propose a conversational agent that grounds external knowledge and persona simultaneously and a retrieval augmented generation model that generates utterances with lesser hallucination and more engagingness. |
| Outcome: | The proposed agent generates the utterance with lesser hallucination and more engagingness utilizing retrieval augmented generation with knowledge-persona enhanced query. |
Towards Diverse and Effective Question-Answer Pair Generation from Children Storybooks (2023.findings-acl)
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Sugyeong Eo, Hyeonseok Moon, Jinsung Kim, Yuna Hur, Jeongwook Kim, SongEun Lee, Changwoo Chun, Sungsoo Park, Heuiseok Lim
| Challenge: | Recent advances in QA pair generation (QAG) have raised interest in applying this technique to the educational field. |
| Approach: | They propose a QAG framework that enhances QA type diversity by producing different interrogative sentences and implicit/explicit answers. |
| Outcome: | The proposed framework outperforms state-of-the-art methods by significant margins, achieving improved diversity and quality. |
I Don’t Need Solution. I Need Emotional Support : Empathetic LLMs based on Emotional Validation (2026.findings-acl)
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| Challenge: | Existing large language models (LLMs) struggle to generate emotional support response, despite observing and reflecting on the help-seeker’s situation . Empathy drives the formation of constructive interpersonal and supportive relationships, including counseling for mental health care . |
| Approach: | They propose to use a two-stage training process to enhance empathetic response generation through empathy acquisition and emotional validation alignment. |
| Outcome: | The proposed method significantly improves empathetic response generation, achieving superior performance in both automatic and human evaluations. |
Post-hoc Utterance Refining Method by Entity Mining for Faithful Knowledge Grounded Conversations (2023.emnlp-main)
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Yoonna Jang, Suhyune Son, Jeongwoo Lee, Junyoung Son, Yuna Hur, Jungwoo Lim, Hyeonseok Moon, Kisu Yang, Heuiseok Lim
| Challenge: | Despite advances in language generation, models suffer from hallucinations that are either untrue or unfaithful to a given source. |
| Approach: | They propose a method to refine hallucinated utterances based on source knowledge . REM implicitly uses key entities in the knowledge to refine the utterant . |
| Outcome: | The proposed method reduces entity hallucination in the generated utterance and improves the quality of the model. |
Beyond Candidates : Adaptive Dialogue Agent Utilizing Persona and Knowledge (2023.findings-emnlp)
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| Challenge: | a previous study suggested that human dialogue systems ground persona and knowledge but they require incomplete candidate sets. |
| Approach: | They propose an adaptive dialogue agent that uses persona and knowledge without candidate sets . their model generates consistent and relevant persona descriptions and identifies relevant knowledge . |
| Outcome: | The proposed model outperforms baselines that ground persona and knowledge candidates even with fragmentary information. |