1 citations · 1 across the 6 of their papers we have counts for
6 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…
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…
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…
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…