Papers by Kyeonghyun Kim
Aligning with Your Own Voice: Self-Corrected Preference Learning for Hallucination Mitigation in LVLMs (2026.findings-acl)
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| Challenge: | Existing preference learning-based approaches rely on proprietary models to construct preference datasets, causing a distributional mismatch between the proprietary and target models. |
| Approach: | They propose a framework that aligns LVLMs using in-distribution data derived from the model's intrinsic knowledge. |
| Outcome: | The proposed framework surpasses baselines in hallucination mitigation while requiring only 5.2k samples. |
Plug-in and Fine-tuning: Bridging the Gap between Small Language Models and Large Language Models (2025.acl-long)
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| Challenge: | Large language models (LLMs) are renowned for their extensive linguistic knowledge and strong generalization capabilities, but their high computational demands make them unsuitable for resource-constrained environments. |
| Approach: | They propose a framework that integrates a single frozen layer from an LLM into a SLM and fine-tunes the combined model for specific tasks. |
| Outcome: | The proposed framework improves performance across a range of natural language processing tasks, including both natural language understanding and generation. |
Making Sense of Korean Sentences: A Comprehensive Evaluation of LLMs through KoSEnd Dataset (2025.acl-srw)
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| Challenge: | Despite advances in LLMs, there are still concerns about their effectiveness with low-resource agglutinative languages compared to English. |
| Approach: | They evaluated 11 LLMs to assess their understanding of Korean sentence endings . they found that explicitly considering linguistic features improved performance . |
| Outcome: | The evaluated LLMs were able to understand Korean sentences better than other languages. |