Papers by Janghan Yoon
Are Any-to-Any Models More Consistent Across Modality Transfers Than Specialists? (2025.acl-long)
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| Challenge: | equivariance evaluations uncover weak but observable consistency through structured analyses of the intermediate latent space enabled by multiple editing operations. |
| Approach: | They use a dataset of 1,000 images paired with captions, editing instructions, and Q&A pairs to evaluate cross-modal transfers rigorously. |
| Outcome: | The proposed models do not consistently demonstrate greater cross-modal consistency than specialized models in pointwise evaluations such as cyclic consistency. |
Do MLLMs Capture How Interfaces Guide User Behavior? A Benchmark for Multimodal UI/UX Design Understanding (2026.acl-long)
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Jaehyun Jeon, Min Soo Kim, Janghan Yoon, Sumin Shim, Yejin Choi, Hanbin Kim, Dae Hyun Kim, Youngjae Yu
| Challenge: | Recent studies focus on surface-level features, overlooking how design choices influence user behavior at scale. |
| Approach: | They propose a benchmark for multimodal understanding of how UI/UX design affects user behavior built on 300 real-world UI image pairs from industry A/B tests. |
| Outcome: | The proposed benchmarks show that models exhibit limited understanding of the behavioral impact of UI/UX design. |
C2: Scalable Auto-Feedback for LLM-based Chart Generation (2025.naacl-long)
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Woosung Koh, Janghan Yoon, MinHyung Lee, Youngjin Song, Jaegwan Cho, Jaehyun Kang, Taehyeon Kim, Se-Young Yun, Youngjae Yu, Bongshin Lee
| Challenge: | generating high-quality charts with Large Language Models presents significant challenges due to limited data and the high cost of curation. |
| Approach: | They propose a referencefree automatic feedback generator to generate high-quality charts with Large Language Models. |
| Outcome: | The proposed framework outperforms baselines and shows that it significantly improves data diversity. |
Zero-shot Multimodal Document Retrieval via Cross-modal Question Generation (2025.emnlp-main)
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| Challenge: | Existing multimodal large language models struggle when faced with unseen domains or languages. |
| Approach: | They propose a framework that leverages the broad knowledge of an MLLM to generate cross-modal pre-questions (preQs) before retrieval. |
| Outcome: | Experiments show that PREMIR outperforms existing retrievers on out-of-distribution benchmarks, including closed-domain and multilingual settings, outperforming strong baselines across all metrics. |