Papers by Kijung Shin
Pre-Deployment Advertisement Ranking under Data Scarcity via Context-Aware Criteria Generation with VLMs (2026.acl-industry)
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Kyungho Kim, Yeonje Choi, Gyurim Hwang, Sejin Chung, Hongseok Lee, Myeong Ho Song, Yeongho Kim, Sunwoo Kim, Jongha Lee, Juyeon Kim, Kijung Shin
| Challenge: | Existing VLMs perform well on general multimodal tasks, but limited labeled data makes them difficult to apply to real-world business decisions. |
| Approach: | They propose a new task that aims to rank ads for a target brand prior to deployment . they propose 'brand-specific ad ranking' which uses brand-specific effectiveness . |
| Outcome: | The proposed task outperforms baselines on 10 brands on real-world advertising data. |
Hybrid-Vector Retrieval for Visually Rich Documents: Combining Single-Vector Efficiency and Multi-Vector Accuracy (2026.findings-acl)
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| Challenge: | Existing approaches to document retrieval are coarse and efficient, but expensive. |
| Approach: | a plug-and-play two-stage hybrid-vector framework is proposed to retrieve visually rich documents . HEAVEN efficiently retrieves candidate pages using a single-vektor method over VS-Pages . it also reranks candidates with a multi-vecctor method while filtering query tokens by linguistic importance . |
| Outcome: | HEAVEN achieves 99.87% of the Recall@1 performance of multi-vector models on average . it reduces per-query computation by 99.8%, achieving efficiency and accuracy . |
‘Hello, World!’: Making GNNs Talk with LLMs (2025.findings-emnlp)
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| Challenge: | graph neural networks have shown remarkable performance across diverse graph-related tasks, but their high-dimensional hidden representations render them black boxes. |
| Approach: | They propose a graph-based neural network with hidden representations in the form of human-readable text. |
| Outcome: | The proposed GNN outperforms existing LLM-based baseline methods on node classification and link prediction. |