Papers by Jaehoon Yun
Benchmarking Direct Preference Optimization for Medical Large Vision–Language Models (2026.findings-eacl)
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| Challenge: | Large vision-language models (LVLMs) are gaining traction in clinical tasks such as diagnostic support, report generation, and medical question answering. |
| Approach: | They present a systematic evaluation of nine DPO variants applied to two leading medical LVLMs. |
| Outcome: | The proposed model improves alignment and reduces severe hallucinations, but yields inconsistent gains over supervised fine-tuning. |
Med-PRM: Medical Reasoning Models with Stepwise, Guideline-verified Process Rewards (2025.emnlp-main)
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Jaehoon Yun, Jiwoong Sohn, Jungwoo Park, Hyunjae Kim, Xiangru Tang, Daniel Shao, Yong Hoe Koo, Ko Minhyeok, Qingyu Chen, Mark Gerstein, Michael Moor, Jaewoo Kang
| Challenge: | Large language models have shown promise in clinical decision making, but current approaches struggle to localize and correct reasoning errors at specific steps of the reasoning process. |
| Approach: | They propose a process reward modeling framework that leverages retrieval-augmented generation to verify each reasoning step against established medical knowledge bases. |
| Outcome: | The proposed model improves on five medical QA benchmarks and two open-ended diagnostic tasks by 13.50% on MedQA. |
Synergy with Translation Artifacts for Training and Inference in Multilingual Tasks (2022.emnlp-main)
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| Challenge: | Recent work has shown promising transferability of pre-trained multilingual language models. |
| Approach: | They propose a cross-lingual fine-tuning algorithm that uses SupCon and MixUp to combine them to improve performance. |
| Outcome: | The proposed algorithm improves cross-lingual transferability by using SupCon and MixUp. |
BAPO: Base-Anchored Preference Optimization for Overcoming Forgetting in Large Language Models Personalization (2024.findings-emnlp)
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| Challenge: | Existing approaches to align Large Language Models with human preferences fail to maintain general knowledge and alignment when faced with personalized preferences. |
| Approach: | They propose a method that utilizes the initial responses of the reference model to mitigate forgetting while accommodating personalized alignment. |
| Outcome: | The proposed approach mitigates forgetting while accommodating personalized alignment while preserving global knowledge and general alignment. |