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cs.LG2026
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…
cs.LG2025
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…
cs.LG2024
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…