Papers by Hyeonseok Kang
Guidance-Based Prompt Data Augmentation in Specialized Domains for Named Entity Recognition (2024.acl-short)
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| Challenge: | specialized fields such as science and biology face significant challenges due to the scarcity of quality data. |
| Approach: | They propose a guidance data augmentation technique that abstracts context and sentence structure and maintains context-entity relationships for DA. |
| Outcome: | The proposed method enhances the training performance of named entity recognition tasks while maintaining context-entity relationships. |
AMACE: Automatic Multi-Agent Chart Evolution for Iteratively Tailored Chart Generation (2025.emnlp-main)
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| Challenge: | Recent studies have shown that chart generation requires manual input of code and intent . despite the benefits of large language models, chart generation still requires user input of many parameters . |
| Approach: | They propose a loop-based framework for automatically evolving charts in a multi-agent environment using large language models. |
| Outcome: | The proposed framework improves performance by 29.97% compared to first generation while reducing generation time by 86.9% compared with manual prompt-based methods. |
R-GDA: Reflective Guidance Data Augmentation with Multi-Agent Feedback for Domain-Specific Named Entity Recognition (2026.findings-eacl)
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| Challenge: | Named Entity Recognition (NER) tasks require data augmentation due to the scarcity of annotated corpora. |
| Approach: | They propose a framework that introduces a multi-agent feedback loop to enhance augmentation quality. |
| Outcome: | The proposed framework improves on SciERC and NCBI-disease datasets and achieves low BERTScore in most cases. |