Papers by Nayeon Kim
Exploring the Impact of Corpus Diversity on Financial Pretrained Language Models (2023.findings-emnlp)
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| Challenge: | Existing financial PLMs are not pretrained on sufficiently diverse financial data, leading to subpar generalization performance. |
| Approach: | They propose to pretrain financial PLMs on financial corpus and train financial models on financial data. |
| Outcome: | The proposed financial language models outperform existing financial PLMs on financial tasks even for unseen corpus groups. |
Break it Down into BTS: Basic, Tiniest Subword Units for Korean (2022.emnlp-main)
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| Challenge: | Existing word embeddings for Korean use the internal structure of words with subword information to improve the quality of word representations. |
| Approach: | They introduce Basic, Tiniest Subword (BTS) units for Korean language that are inspired by Hangeul, the Korean writing system. |
| Outcome: | The proposed framework outperforms the state-of-the-art Korean word embedding by 11.8% on all intrinsic and extrinsic tasks. |
Clinical Note Owns its Hierarchy: Multi-Level Hypergraph Neural Networks for Patient-Level Representation Learning (2023.acl-long)
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| Challenge: | Clinical notes of patient EHRs contain valuable information from healthcare professionals, but have been underutilized due to their difficult-to-understand contents and complex hierarchies. |
| Approach: | They propose to use clinical notes to learn more balanced knowledge from EHRs by assembling useful neutral words with rare keywords via note and taxonomy level hyperedges. |
| Outcome: | The proposed method can retain clinical semantic information by (1) frequent neutral words and (2) hierarchies with imbalanced distribution. |
Exploring Cross-Cultural Differences in English Hate Speech Annotations: From Dataset Construction to Analysis (2024.naacl-long)
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| Challenge: | Existing datasets for hate speech detection neglect the cultural diversity within a single language. |
| Approach: | They propose a CR**oss-cultural **E**nglish **Hate* speech dataset that uses culturally hateful keywords to identify posts from four countries plus the United States. |
| Outcome: | The proposed dataset shows that only 56.2% of the posts in CREHate achieve consensus among all countries, with the highest pairwise label difference rate of 26%. |
Polishing Every Facet of the GEM: Testing Linguistic Competence of LLMs and Humans in Korean (2025.acl-long)
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| Challenge: | Existing studies have focused on linguistic competence of language models with grammatical knowledge. |
| Approach: | They propose to use grammar as a measurable proxy to assess linguistic competence of large language models (LLMs) . |
| Outcome: | The proposed model aims to assess the linguistic competence of large language models (LLMs) and humans in Korean. |