Papers by Junhee Cho
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. |
MapCoder-Lite: Distilling Multi-Agent Coding into a Single Small LLM (2026.findings-eacl)
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| Challenge: | Existing large-scale (> 30 B) models are costly and collapse when downsized to small open-source models. |
| Approach: | They propose a framework for distilling large, multi-agent coding systems into a single 7B model. |
| Outcome: | The proposed framework doubles xCodeEval accuracy and reduces GPU memory and token generation time by 4 compared to a 32B model. |
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. |