Papers by SungHo Kim
SEED: Semantic Knowledge Transfer for Language Model Adaptation to Materials Science (2024.emnlp-industry)
Copied to clipboard
| Challenge: | Existing methods to adapt pre-trained language models to materials science rely on frequency information from limited downstream datasets. |
| Approach: | They propose a vocabulary expansion method to adapt pre-trained language models to materials science by incorporating latent materials knowledge of lightweight embeddings into PLMs. |
| Outcome: | The proposed method mitigates the limitations of existing adaptation methods and can be used in materials science. |
KOMBO: Korean Character Representations Based on the Combination Rules of Subcharacters (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing pre-trained language models for Korean have overlooked character representation principles . a new framework for Korean PLMs uses the invention principles of Hangeul to represent letters . |
| Approach: | They propose a framework that uses Hangeul's invention principles to represent character in Korean . their method outperforms the state-of-the-art Korean PLM by 2.11% in five tasks . |
| Outcome: | The proposed method outperforms the state-of-the-art Korean PLM by 2.11% in five Korean natural language understanding tasks. |
Incorporating Domain Knowledge into Materials Tokenization (2025.acl-long)
Copied to clipboard
| Challenge: | Recent advances in language models have expanded their applications in materials science, but they often produce excessive fragmentation and semantic loss. |
| Approach: | They propose a frequency-centric tokenization approach that integrates material knowledge into tokenization. |
| Outcome: | The proposed tokenization approach outperforms existing tokenization methods and achieves an average performance gain of 4% and 2% in the generation and classification tasks. |
Polishing Every Facet of the GEM: Testing Linguistic Competence of LLMs and Humans in Korean (2025.acl-long)
Copied to clipboard
| 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. |
SCRIPT: A Subcharacter Compositional Representation Injection Module for Korean Pre-Trained Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Korean uses a featural writing system in which each character is composed of subcharacter units known as Jamo. |
| Approach: | They propose a model-agnostic module that injects subcharacter compositional knowledge into Korean language models. |
| Outcome: | a new module improves embeddings of Korean subwords with structural granularity . the module improve grammatical regularities and semantic cohesive variations . |