My primary research lies in the area of natural language processing and efficient artificial intelligence. I am particularly interested in the sustainability and truthfulness of language models, which have opened promising avenues for research including:

Language Agents: Reasoning, memory, and planning with multimodal language models.

Efficient Language Models: Reduction of the computational and memory complexities in language models, while maintaining their performance on downstream tasks

Augmented Language Models: Fast knowledge learning and editing of language models from symbolic resources, such as knowledge graphs, texts, and tools

최종학력

Ph.D. in Computer Science and Engineering, Korea University

전공분야

Deep Learning

주요 연구

• Efficient Language Model
• Augmented Language Models
• Complex Reasoning with Language Models

주요 강의

• Natural Language Processing
• Machine Learning
• Programming
• Information Retrieval
• Data Science

주요 논문/저서

(* denotes equal contribution)

(2025)

• Yerim Oh, Jun-Hyung Park, Junho Kim, SungHo Kim, SangKeun Lee. Incorporating Domain Knowledge into Materials Tokenization. ACL 2025.
• Nayeon Kim*, Eojin Jeon*, Jun-Hyung Park, SangKeun Lee. Handling Korean Out-of-Vocabulary Words with Phoneme Representation Learning. PAKDD 2025.
• Mingyu Lee, Junho Kim, Jun-Hyung Park, SangKeun Lee. Continual Debiasing: A Bias Mitigation Framework for Natural Language Understanding Systems. ESWA.

(2024)

• Jun-Hyung Park, Yeachan Kim, Mingyu Lee, Hyuntae Park, SangKeun Lee. MolTRES: Improving Chemical Language Representation Learning for Molecular Property Prediction. EMNLP 2024.
• Hyuntae Park*, Yeachan Kim*, Jun-Hyung Park, SangKeun Lee. Zero-shot Commonsense Reasoning over Machine Imagination. Findings of EMNLP 2024.
• Junho Kim*, Yeachan Kim*, Jun-Hyung Park, Yerim Oh, Suho Kim, SangKeun Lee. MELT: Materials-aware Continued Pre-training for Language Model Adaptation to Materials Science. Findings of EMNLP 2024.
• Jun-Hyung Park*, Hyuntae Park*, Yeachan Kim, Woosang Lim, SangKeun Lee. Moleco: Molecular Contrastive Learning with Chemical Language Models for Molecular Property Prediction. EMNLP 2024 Industry.
• Yeachan Kim, Jun-Hyung Park, SungHo Kim, Juhyeong Park, Sangyun Kim, SangKeun Lee. SEED: Semantic Knowledge Transfer for Language Model Adaptation to Materials Sciences. EMNLP 2024 Industry.
• Jun-Hyung Park, Mingyu Lee, Junho Kim, and SangKeun Lee. Coconut: Contextualized Commonsense Unified Transformers for Graph-Based Commonsense Augmentation of Language Models. Findings of ACL 2024.

(2023)

• Jun-Hyung Park*, Hyuntae Park*, Youjin Kang, Eojin Jeon, and SangKeun Lee. DIVE: Towards Descriptive and Diverse Visual Commonsense Generation. EMNLP 2023.

• Yeachan Kim, Junho Kim, Jun-Hyung Park, Mingyu Lee, and SangKeun Lee. Leap-of-Thought: Accelerating Transformers via Dynamic Token Routing. EMNLP 2023.

• Joon-Young Choi, Junho Kim, Jun-Hyung Park, Wing-Lam Mok, and SangKeun Lee. SMoP: Towards Efficient and Effective Prompt Tuning with Sparse Mixture-of-Prompts. EMNLP 2023.

• Yeachan Kim*, Junho Kim*, Wing-Lam Mok, Jun-Hyung Park and SangKeun Lee. Client-Customized Adaptation for Parameter-Efficient Federated Learning. Findings of ACL 2023.

• Jun-Hyung Park, Yeachan Kim, Junho Kim, Joon-Young Choi, and SangKeun Lee. Dynamic Structure Pruning for Compressing CNNs. AAAI 2023.

(2022)

• Jun-Hyung Park*, Mingyu Lee*, Junho Kim, Kang-Min Kim, and SangKeun Lee. Efficient Pre-training of Masked Language Model via Concept-based Curriculum Masking. EMNLP 2022. 

• Jun-Hyung Park*, Junho Kim*, Mingyu Lee, Wing-Lam Mok, Joon-Young Choi, and SangKeun Lee. Tutoring Helps Students Learn Better: Improving Knowledge Distillation for BERT with Tutor Network. EMNLP 2022.

• Jun-Hyung Park*, Nayeon Kim*, Joon-Young Choi, Eojin Jeon, Youjin Kang, and SangKeun Lee. Break it Down into BTS: Basic, Tiniest Subword Units for Korean. EMNLP 2022.

• Jun-Hyung Park, Kang-Min Kim, and SangKeun Lee. Quantized Sparse Training: A Unified Trainable Framework for Joint Pruning and Quantization of DNNs. ACM TECS.

• Jun-Hyung Park*, Yong-Ho Jung*, Joon-Young Choi, Mingyu Lee, Junho Kim, Kang-Min Kim, and SangKeun Lee. Learning from Missing Relations: Contrastive Learning with Commonsense Knowledge Graphs for Commonsense Inference. Findings of ACL 2022. 

• Jun-Hyung Park, Byung-Ju Choi, and SangKeun Lee. Examining the Impact of Adaptive Convolution on Natural Language Understanding. ESWA.