Papers by Siun Kim
DiZiNER: Disagreement-guided Instruction Refinement via Simulating Pilot Annotation for Zero-shot Named Entity Recognition (2026.acl-long)
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| Challenge: | Large language models have advanced information extraction (IE) by enabling zero-shot and few-shot named entity recognition (NER) but their outputs still show persistent and systematic errors. |
| Approach: | They propose a framework that simulates the pilot annotation process and employs LLMs as both annotators and supervisors to refine model disagreements. |
| Outcome: | Using a pilot annotation process, the proposed framework outperforms its supervisor model on 18 benchmarks. |
CReSE: Benchmark Data and Automatic Evaluation Framework for Recommending Eligibility Criteria from Clinical Trial Information (2024.findings-eacl)
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| Challenge: | Eligibility criteria (EC) are defined as a set of conditions an individual must meet to participate in a clinical trial. |
| Approach: | They propose to recommend EC based on clinical trial information, including trial titles, and introduce an automatic evaluation framework to assess clinical validity of the EC recommendation model. |
| Outcome: | The proposed model outperforms existing language models pre-trained on the biomedical domain in EC clustering. |