Janghan Yoon — accepted AI-conference papers

2025

Are Any-to-Any Models More Consistent Across Modality Transfers Than Specialists?

Jiwan Chung, Janghan Yoon, Junhyeong Park, Sangeyl Lee, Joowon Yang, Sooyeon Park +1

ACL 2025long

Any-to-any generative models aim to enable seamless interpretation and generation across multiple modalities within a unified framework, yet their ability to preserve relationships across modalities remains uncertain. Do unified models truly achieve cross-modal coherence, or is this coherence merely…

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2025

Zero-shot Multimodal Document Retrieval via Cross-modal Question Generation

Yejin Choi, Jaewoo Park, Janghan Yoon, Saejin Kim, Jaehyun Jeon, Youngjae Yu

EMNLP 2025

Rapid advances in Multimodal Large Language Models (MLLMs) have extended information retrieval beyond text, enabling access to complex real-world documents that combine both textual and visual content. However, most documents are private, either owned by individuals or confined within corporate silo

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