| 2025 | ACL | Forward Knows Efficient Backward Path: Saliency-Guided Memory-Efficient Fine-tuning of Large Language Models. | Yeachan Kim, SangKeun Lee |
| 2025 | ACL | Curriculum Debiasing: Toward Robust Parameter-Efficient Fine-Tuning Against Dataset Biases. | Mingyu Lee, Yeachan Kim, Wing-Lam Mok, SangKeun Lee |
| 2025 | EMNLP | Bridging the Gap Between Molecule and Textual Descriptions via Substructure-aware Alignment. | Hyuntae Park, Yeachan Kim, SangKeun Lee |
| 2024 | ACL | SparseFlow: Accelerating Transformers by Sparsifying Information Flows. | Yeachan Kim, SangKeun Lee |
| 2024 | ACL | Towards Robust and Generalized Parameter-Efficient Fine-Tuning for Noisy Label Learning. | Yeachan Kim, Junho Kim, SangKeun Lee |
| 2024 | ACL | KOMBO: Korean Character Representations Based on the Combination Rules of Subcharacters. | SungHo Kim, Juhyeong Park, Yeachan Kim, SangKeun Lee |
| 2024 | EMNLP | MELT: Materials-aware Continued Pre-training for Language Model Adaptation to Materials Science. | Junho Kim, Yeachan Kim, Jun-Hyung Park, Yerim Oh, Suho Kim, SangKeun Lee |
| 2024 | EMNLP | SEED: Semantic Knowledge Transfer for Language Model Adaptation to Materials Science. | Yeachan Kim, Jun-Hyung Park, SungHo Kim, Juhyeong Park, Sangyun Kim, SangKeun Lee |
| 2024 | EMNLP | MolTRES: Improving Chemical Language Representation Learning for Molecular Property Prediction. | Jun-Hyung Park, Yeachan Kim, Mingyu Lee, Hyuntae Park, SangKeun Lee |
| 2024 | EMNLP | Zero-shot Commonsense Reasoning over Machine Imagination. | Hyuntae Park, Yeachan Kim, Jun-Hyung Park, SangKeun Lee |
| 2024 | EMNLP | Moleco: Molecular Contrastive Learning with Chemical Language Models for Molecular Property Prediction. | Jun-Hyung Park, Hyuntae Park, Yeachan Kim, Woosang Lim, SangKeun Lee |
| 2023 | AAAI | Dynamic Structure Pruning for Compressing CNNs. | Jun-Hyung Park, Yeachan Kim, Junho Kim, Joon-Young Choi, SangKeun Lee |
| 2023 | ACL | Client-Customized Adaptation for Parameter-Efficient Federated Learning. | Yeachan Kim, Junho Kim, Wing-Lam Mok, Jun-Hyung Park, SangKeun Lee |
| 2023 | EMNLP | Improving Bias Mitigation through Bias Experts in Natural Language Understanding. | Eojin Jeon, Mingyu Lee, Juhyeong Park, Yeachan Kim, Wing-Lam Mok, SangKeun Lee |
| 2023 | EMNLP | Leap-of-Thought: Accelerating Transformers via Dynamic Token Routing. | Yeachan Kim, Junho Kim, Jun-Hyung Park, Mingyu Lee, SangKeun Lee |
| 2023 | UAI | Phase-shifted adversarial training. | Yeachan Kim, Seongyeon Kim, Ihyeok Seo, Bonggun Shin |
| 2022 | KDD | In Defense of Core-set: A Density-aware Core-set Selection for Active Learning. | Yeachan Kim, Bonggun Shin |
| 2022 | LREC | Context-based Virtual Adversarial Training for Text Classification with Noisy Labels. | Do-Myoung Lee, Yeachan Kim, Chang-gyun Seo |
| 2020 | ACL | Adaptive Compression of Word Embeddings. | Yeachan Kim, Kang-Min Kim, SangKeun Lee |
| 2020 | EMNLP | Multi-pretraining for Large-scale Text Classification. | Kang-Min Kim, Bumsu Hyeon, Yeachan Kim, Jun-Hyung Park, SangKeun Lee |
| 2020 | LREC | Representation Learning for Unseen Words by Bridging Subwords to Semantic Networks. | Yeachan Kim, Kang-Min Kim, SangKeun Lee |
| 2020 | SAC | Personalizing large-scale text classification by modeling individual differences. | Jungho Lee, Byung-Ju Choi, Yeachan Kim, Kang-Min Kim, Woo-Jong Ryu, SangKeun Lee |
| 2019 | WWW | From Small-scale to Large-scale Text Classification. | Kang-Min Kim, Yeachan Kim, Jungho Lee, Ji-Min Lee, SangKeun Lee |
| 2018 | COLING | Learning to Generate Word Representations using Subword Information. | Yeachan Kim, Kang-Min Kim, Ji-Min Lee, SangKeun Lee |