Papers by Geunyeong Jeong
STEAM: A Semantic-Level Knowledge Editing Framework for Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing methods for locate-and-editing focus on token-level likelihood optimization without addressing semantic coherence. |
| Approach: | They propose a semantic-level knowledge editing framework that enhances integration of updated knowledge into the model's knowledge structure. |
| Outcome: | The proposed framework improves integration of updated knowledge into the model's knowledge structure and improves semantic coherence. |
Exploring the Impact of Instruction-Tuning on LLM’s Susceptibility to Misinformation (2025.acl-long)
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| Challenge: | Existing studies highlight that large language models are receptive to external information that contradicts their parametric knowledge, but little research has been conducted on the direct impact of instruction-tuning on this phenomenon. |
| Approach: | They examine how instruction-tuning influences LLMs' susceptibility to misinformation, particularly in knowledge conflict situations. |
| Outcome: | The proposed model is more user-oriented and more likely to accept misinformation when it is presented by the user. |
Bridging the Code Gap: A Joint Learning Framework across Medical Coding Systems (2024.lrec-main)
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| Challenge: | Existing methods for automating medical coding focus on a single coding system . however, there are still challenges to overcome in coding. |
| Approach: | They propose a joint learning framework for Across Medical coding systems which jointly learns different coding system through multi-task learning. |
| Outcome: | The proposed framework improves the performance of the MIMIC-IV ICD-9 and MIMICIV I CD-10 datasets. |