Papers by Junhee Park
Watermarking for Factuality: Guiding Vision-Language Models Toward Truth via Tri-layer Contrastive Decoding (2025.findings-emnlp)
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Kyungryul Back, Seongbeom Park, Milim Kim, Mincheol Kwon, SangHyeok Lee, Hyunyoung Lee, Junhee Cho, Seunghyun Park, Jinkyu Kim
| Challenge: | Large Vision-Language Models (LVLMs) have shown promising results on multimodal tasks, but remain prone to hallucinations due to their reliance on a single modality or memorizing training data without properly grounding their outputs. |
| Approach: | They propose a training-free, tri-layer contrastive decoding with watermarking that uses a watermark-related question to identify a pivot layer and apply tri-layered contrastive coding to generate the final output. |
| Outcome: | The proposed method reduces hallucinations and generates more visually grounded responses. |
GuideDog: A Real-World Egocentric Multimodal Dataset for Blind and Low-Vision Accessibility-Aware Guidance (2026.acl-long)
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Junhyeok Kim, Jaewoo Park, Junhee Park, Sangeyl Lee, Jiwan Chung, Jisung Kim, Ji Hoon Joung, Youngjae Yu
| Challenge: | Recent advances in multimodal large language models (MLLMs) offer new opportunities for higher-level scene understanding, but they require labor-intensive, expert annotation. |
| Approach: | They propose a dataset that combines 2K human-verified images with 22K image-description pairs to provide a more accurate representation of pedestrian scenes. |
| Outcome: | The proposed dataset improves scalability while maintaining quality. |
Towards standardizing Korean Grammatical Error Correction: Datasets and Annotation (2023.acl-long)
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| Challenge: | Despite the growing number of Korean learners, little research has been conducted on Korean grammatical error correction (GEC) despite the difficulties of the Korean language, there is no evaluation benchmark for Korean GEC. |
| Approach: | They propose to use Korean grammar error correction datasets to train a machine learning model that can automatically annotate Korean errors from parallel corpora. |
| Outcome: | The proposed model outperforms the currently used statistical Korean GEC system on a wider range of error types. |