Papers by Hongseok Choi
GENDEX: Generative Data Augmentation Strategy Leveraging External Data for Abstractive Dialogue Summarization (2024.findings-acl)
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| Challenge: | Existing methods to summarize text data are limited by the lack of data. |
| Approach: | They propose a method that uses external data to generate synthetic dialogues from short texts containing people and their interpersonal interactions. |
| Outcome: | The proposed method shows robust performance, generalizability, and scalability regardless of complexity of dialogues. |
Taxation Perspectives from Large Language Models: A Case Study on Additional Tax Penalties (2026.eacl-long)
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| Challenge: | Large language models (LLMs) have demonstrated promising results across various domains, including the legal domain. |
| Approach: | They propose a benchmark to assess the ability of large language models to predict the legitimacy of additional tax penalties. |
| Outcome: | The proposed model is based on 100 Korean court precedents and 100 binary-choice questions. |
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. |