Papers by Jiwoo Kim
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. |
It Ain’t Over: A Multi-aspect Diverse Math Word Problem Dataset (2023.emnlp-main)
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| Challenge: | Existing studies lack diversity in problem types, lexical usage patterns, languages, and intermediate solution forms for the math word problem. |
| Approach: | They propose a new MWP dataset with a wide range of diversity in problem types, lexical usage patterns, languages, and intermediate solutions. |
| Outcome: | The proposed dataset provides an opportunity to evaluate the capability of large language models. |
Online Difficulty Filtering for Reasoning Oriented Reinforcement Learning (2026.eacl-long)
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| Challenge: | Recent advances in reinforcement learning with verifiable rewards (RLVR) show that large language models enhance their reasoning abilities when trained with veriable signals. |
| Approach: | They propose a method for a problem-aware filtering system that maximizes learning efficiency by selecting tasks of intermediate difficulty. |
| Outcome: | The proposed model improves when trained with verifiable rewards, but training efficiency is bottleneck . the proposed model achieves +12% gains in less than half the training steps of standard GRPO . |
Learning from Negative Samples in Biomedical Generative Entity Linking (2025.findings-acl)
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| Challenge: | Generative models are usually trained only with positive samples and do not explicitly learn from hard negative samples, which are entities that look similar but have different meanings. |
| Approach: | They propose a framework that trains generative BioEL models using negative samples to learn from hard negative samples. |
| Outcome: | The proposed framework outperforms baseline models by up to an average top-1 accuracy of 1.4% on five benchmarks. |
Model-based Preference Optimization in Abstractive Summarization without Human Feedback (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) can generate fluent summaries but often introduce inaccuracies by hallucinating content not found in the source document. |
| Approach: | They propose a method to fine-tune Large Language Models for improved summarization abilities without any human feedback. |
| Outcome: | The proposed method significantly improves the quality of generated summaries without any human feedback. |