Papers by Donghoon Shin
KoLEG: On-the-Fly Korean Legal Knowledge Editing with Continuous Retrieval (2025.findings-emnlp)
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Jaehyung Seo, Dahyun Jung, Jaewook Lee, Yongchan Chun, Dongjun Kim, Hwijung Ryu, Donghoon Shin, Heuiseok Lim
| Challenge: | a recent study shows that Korean legal knowledge is subject to frequent temporal updates driven by societal needs and government policies. |
| Approach: | They propose a Korean Legal knowledge editing framework enhanced with continuous retrieval . they employ an Editing-Aware Learning Strategy and a LawEdit Retriever . |
| Outcome: | a new framework outperforms existing methods for updating legal knowledge in Korean . it maintains robust performance in sequential editing and is qualitatively validated by legal experts. |
Persona Expansion with Commonsense Knowledge for Diverse and Consistent Response Generation (2023.eacl-main)
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Donghyun Kim, Youbin Ahn, Wongyu Kim, Chanhee Lee, Kyungchan Lee, Kyong-Ho Lee, Jeonguk Kim, Donghoon Shin, Yeonsoo Lee
| Challenge: | Existing researches have focused on generating diverse and consistent responses based on personal traits. |
| Approach: | They propose a consistent persona expansion framework that improves not only the diversity but also the consistency of persona-based responses. |
| Outcome: | The proposed framework improves not only the diversity but also the consistency of persona-based responses on the Persona-Chat dataset. |
Concept-based Persona Expansion for Improving Diversity of Persona-Grounded Dialogue (2023.eacl-main)
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| Challenge: | Existing approaches to improve the quality of persona-grounded dialogues are limited to a few informative words. |
| Approach: | They propose a concept-based persona expansion framework that takes the original persona as input and generates expanded personas that contain conceptually rich content. |
| Outcome: | The proposed framework improves the quality of persona-grounded dialogue responses in diversity and richness. |
Personality Editing for Language Models through Adjusting Self-Referential Queries (2026.eacl-long)
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| Challenge: | Large Language Models (LLMs) are integral to conversational agents and content creation, but they lack robustness and require large-scale training data to achieve significant improvements in personality alignment. |
| Approach: | They propose a method that introduces adjustment queries where self-referential statements grounded in psychological constructs are treated analogously to factual knowledge to enable direct editing of personality-related responses. |
| Outcome: | The proposed method improves personality alignment across personality dimensions and requires only 12 editing samples to achieve significant improvements. |