1 citations · 2 across the 10 of their papers we have counts for
11 papers
MolSC: Leveraging Substituent Contributions to Enhance Fine-grained Molecular Understanding in LLMs
Hyuntae Park, Sooyeon Kim, Jiwon Park +1
Recent advances in natural language processing have led to molecular Large Language Models (LLMs) with strong performance across diverse chemistry tasks. However, they still strugg…
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…
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)…
Zero-shot Commonsense Reasoning over Machine Imagination
Hyuntae Park, Yeachan Kim, Jun-Hyung Park +1
Recent approaches to zero-shot commonsense reasoning have enabled Pre-trained Language Models (PLMs) to learn a broad range of commonsense knowledge without being tailored to speci…
Mentor-KD: Making Small Language Models Better Multi-step Reasoners
Hojae Lee, Junho Kim, SangKeun Lee
Large Language Models (LLMs) have displayed remarkable performances across various complex tasks by leveraging Chain-of-Thought (CoT) prompting. Recently, studies have proposed a K…
DIVE: Towards Descriptive and Diverse Visual Commonsense Generation
Jun-Hyung Park, Hyuntae Park, Youjin Kang +2
Towards human-level visual understanding, visual commonsense generation has been introduced to generate commonsense inferences beyond images. However, current research on visual co…
MolTRES: Improving Chemical Language Representation Learning for Molecular Property Prediction
Jun-Hyung Park, Yeachan Kim, Mingyu Lee +2
Chemical representation learning has gained increasing interest due to the limited availability of supervised data in fields such as drug and materials design. This interest partic…
Improving Bias Mitigation through Bias Experts in Natural Language Understanding
Eojin Jeon, Mingyu Lee, Juhyeong Park +3
Biases in the dataset often enable the model to achieve high performance on in-distribution data, while poorly performing on out-of-distribution data. To mitigate the detrimental e…