Hyuhng Joon Kim

About

Hello, I am a Ph.D. candidate in Graduate School of Artificial Intelligence at Seoul National University. I am currently a member of IDS lab. My research interests are natural language processing, in particular, I aim to develop machine learning models adaptable across different domains.

Education

• 2020.09 ~ present : MS. and Ph.D. (integrated) in Graduate School of Artificial Intelligence, Seoul National University
• Advisor : Prof. Sang-goo Lee
• 2014.03 ~ 2020.08 : B.S. in Computer Science and Engineering, Soongsil University

Work Experience

• 2020 : SWE intern in Tomato System
• 2019 : SWE intern in Naver Corp.

Recent Publications (Google Scholar)

1. Aligning Language Models to Explicitly Handle Ambiguity
   Hyuhng Joon Kim, Youna Kim, Cheonbok Park, Junyeob Kim, Choonghyun Park, Kang Min Yoo, Sang-goo Lee, Taeuk Kim
   arXiv preprint
2. Universal Domain Adaptation for Robust Handling of Distributional Shifts in NLP
   Hyuhng Joon Kim, Hyunsoo Cho, Sang-Woo Lee, Junyeob Kim, Choonghyun Park, Sang-goo Lee, Kang Min Yoo, Taeuk Kim
   The 2023 Conference on Empirical Methods in Natural Language Processing (Findings of EMNLP 2023)
3. Prompt-Augmented Linear Probing: Scaling Beyond The Limit of Few-shot In-Context Learners
   Hyunsoo Cho, Hyuhng Joon Kim, Jun Yeob Kim, Sang-Woo Lee, Sang-goo Lee, Kang Min Yoo, Taeuk Kim
   Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI 2023)
4. Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator
   Hyuhng Joon Kim, Hyunsoo Cho, Jun Yeob Kim, Taeuk Kim, Kang Min Yoo, Sang-goo Lee
   Workshop on Large-scale Pre-trained Language Models 2022 (NAACL Workshop)
5. Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations
   Kang Min Yoo, Jun Yeob Kim, Hyuhng Joon Kim, Hyunsoo Cho, Hwiyeol Jo, Sang-Woo Lee, Sang-goo Lee, Taeuk Kim
   2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022)