7 papers
KOMBO: Korean Character Representations Based on the Combination Rules of Subcharacters
SungHo Kim, Juhyeong Park, Yeachan Kim +1
The Korean writing system, \textit{Hangeul}, has a unique character representation rigidly following the invention principles recorded in \textit{Hunminjeongeum}.\footnote{\textit{…
Enhancing Zero-shot Commonsense Reasoning by Integrating Visual Knowledge via Machine Imagination
Hyuntae Park, Yeachan Kim, SangKeun Lee
Recent advancements in zero-shot commonsense reasoning have empowered Pre-trained Language Models (PLMs) to acquire extensive commonsense knowledge without requiring task-specific…
Bridging the Gap Between Molecule and Textual Descriptions via Substructure-aware Alignment
Hyuntae Park, Yeachan Kim, SangKeun Lee
Molecule and text representation learning has gained increasing interest due to its potential for enhancing the understanding of chemical information. However, existing models ofte…
C2A: Client-Customized Adaptation for Parameter-Efficient Federated Learning
Yeachan Kim, Junho Kim, Wing-Lam Mok +2
Despite the versatility of pre-trained language models (PLMs) across domains, their large memory footprints pose significant challenges in federated learning (FL), where the traini…
CleaR: Towards Robust and Generalized Parameter-Efficient Fine-Tuning for Noisy Label Learning
Yeachan Kim, Junho Kim, SangKeun Lee
Parameter-efficient fine-tuning (PEFT) has enabled the efficient optimization of cumbersome language models in real-world settings. However, as datasets in such environments often…
MELT: Materials-aware Continued Pre-training for Language Model Adaptation to Materials Science
Junho Kim, Yeachan Kim, Jun-Hyung Park +3
We introduce a novel continued pre-training method, MELT (MatEriaLs-aware continued pre-Training), specifically designed to efficiently adapt the pre-trained language models (PLMs)…