Papers by Sumin Park
Korean Bio-Medical Corpus (KBMC) for Medical Named Entity Recognition (2024.lrec-main)
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Sungjoo Byun, Jiseung Hong, Sumin Park, Dongjun Jang, Jean Seo, Minseok Kim, Chaeyoung Oh, Hyopil Shin
| Challenge: | Named Entity Recognition (NER) plays a pivotal role in medical Natural Language Processing (NLP) yet, there is no open-source medical NER dataset specifically for Korean. |
| Approach: | They used ChatGPT to construct an open-source Korean NER dataset . they found 20% increase in medical NER performance compared to general Korean ner datasets. |
| Outcome: | The KBMC dataset shows an impressive 20% increase in medical NER performance compared to models trained on general Korean NER datasets. |
LCIRC: A Recurrent Compression Approach for Efficient Long-form Context and Query Dependent Modeling in LLMs (2025.naacl-long)
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| Challenge: | Large language models excel in generating coherent and contextually rich outputs, but their capacity to handle long-form contexts is limited by fixed-length position embeddings. |
| Approach: | They propose a method that enables the efficient processing long-form sequences beyond the model’s length limit through recurrent compression without retraining the entire model. |
| Outcome: | The proposed method significantly improves LLM’s ability to manage extended contexts, making it well-suited for tasks that require both comprehensive context understanding and query relevance. |