Papers by Wonjoong Kim
SIMPLOT: Enhancing Chart Question Answering by Distilling Essentials (2025.findings-naacl)
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| Challenge: | Recent advances in vision-language models have accelerated research into models capable of advanced reasoning based on images. |
| Approach: | They propose a method that leverages vision-language models to convert charts into table format . they use Large Language Model (LLM) for reasoning to extract only the essential information . |
| Outcome: | The proposed method extracts only the elements necessary for chart reasoning without the need for additional annotations or datasets. |
Is Safety Standard Same for Everyone? User-Specific Safety Evaluation of Large Language Models (2025.findings-emnlp)
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Yeonjun In, Wonjoong Kim, Kanghoon Yoon, Sungchul Kim, Mehrab Tanjim, Sangwu Park, Kibum Kim, Chanyoung Park
| Challenge: | Extensive benchmarks evaluate LLM safety relying heavily on general standards . no benchmark datasets exist to evaluate the user-specific safety of LLMs . |
| Approach: | a new benchmark is designed to assess user-specific aspect of LLM safety . authors propose a simple remedy based on chain-of-thought to improve user-specified safety. |
| Outcome: | a new benchmark assesses the user-specific aspect of LLM safety . the proposed solution improves user-specified safety by chain-of-thought . |
Reasoning Structure Matters for Safety Alignment of Reasoning Models (2026.acl-long)
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| Challenge: | Large reasoning models (LRMs) achieve strong performance on complex reasoning tasks but often generate harmful responses to malicious user queries. |
| Approach: | They propose a method that alters the reasoning structure of large reasoning models to achieve effective safety alignment by supervised fine-tuning. |
| Outcome: | The proposed method is practical and generalizable, requiring no complex training or reward design. |