Papers by Jinsu Kim
Adaptive-VP: A Framework for LLM-Based Virtual Patients that Adapts to Trainees’ Dialogue to Facilitate Nurse Communication Training (2025.findings-acl)
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Keyeun Lee, Seolhee Lee, Esther Hehsun Kim, Yena Ko, Jinsu Eun, Dahee Kim, Hyewon Cho, Haiyi Zhu, Robert E. Kraut, Eunyoung E. Suh, Eun-mee Kim, Hajin Lim
| Challenge: | standardized patient (SP) simulations are costly and inflexible. |
| Approach: | They propose a framework that leverages large language models to dynamically adapt VP behavior based on trainee input. |
| Outcome: | The proposed framework reflects real-world communication skills and produces more natural and realistic interactions than existing methods. |
ECoRAG: Evidentiality-guided Compression for Long Context RAG (2025.findings-acl)
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| Challenge: | Existing compression methods do not focus on filtering out non-evidential information, which limit performance in LLMs. |
| Approach: | They propose a framework that leverages external documents through Retrieval-Augmented Generation (RAG) to reduce RAG overhead. |
| Outcome: | Experiments show that ECoRAG outperforms existing compression methods on ODQA tasks. |
Feeling Right vs. Being Right: How AI Sycophancy Affects Value-Laden Deliberation (2026.acl-long)
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| Challenge: | Unlike human flattery, AI sycophancy is intentional and self-interested . scophancies are a byproduct of RLHF's user-preference alignment process . |
| Approach: | They propose to operationalize AI sycophancy as excessive face-saving, either active (preserving positive face through agreement) or passive (preserving negative face by withholding challenge). |
| Outcome: | The findings show that sycophancy is a byproduct of RLHF's user-preference alignment process and that it is not a human trait. |
SPeCtrum: A Grounded Framework for Multidimensional Identity Representation in LLM-Based Agent (2025.naacl-long)
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Keyeun Lee, Seo Hyeong Kim, Seolhee Lee, Jinsu Eun, Yena Ko, Hayeon Jeon, Esther Hehsun Kim, Seonghye Cho, Soeun Yang, Eun-mee Kim, Hajin Lim
| Challenge: | Existing methods for simulating individual identities oversimplify human complexity, leading to incomplete or flattened representations. |
| Approach: | They propose a framework for constructing authentic LLM agent personas by incorporating an individual’s multidimensional self-concept. |
| Outcome: | The framework integrates Social Identity (S), Personal Identity (P), and Personal Life Context (C) components, each contributing distinct yet interconnected aspects of identity. |