Exploring multimodal perception in large language models through perceptual strength ratings
IEEE Access, 13, 176751–176769.
Computational linguist
Hi, I'm Dojun. I study how language models interpret meaning, infer intentions, and use context across languages. My work focuses on computational pragmatics, multilinguality, and LLM evaluation. I aim to connect these insights to the development of more capable language models, bringing linguistic analysis into model development and evaluation.
IEEE Access, 13, 176751–176769.
arXiv:2502.10995
38th Pacific Asia Conference on Language, Information and Computation · December 2024.
2nd GenBench Workshop on Generalisation (Benchmarking) in NLP, 96–119 · November 2024.
LREC-COLING 2024, 11723–11744 · May 2024.
Journal of KIISE, 49(7), 514–520.
arXiv:2211.13776
Korea Computer Congress 2021, 1720–1722 · June 2021.
2021–2023
M.Sc. in Computational Linguistics
Thesis: Multi-score neural machine translation evaluation: beyond single score metrics
Supervisor: Sebastian Padó
2018–2021
B.Tech. Convergence, Human ICT
B.A. in English Language and Literature
Thesis: Korean-English machine translation with multiple tokenization strategy
Supervisor: Harksoo Kim
Dec 2024–Mar 2025
AI Engineer · Seoul, South Korea
Developed an LLM-based Korean–English web novel translation pipeline, including narrative segmentation, automated glossary generation, and cross-system translation quality review.
Nov 2023–Aug 2024
Research Assistant · AI Institute
Worked on MultiPragEval across English, German, Korean, and Chinese; Korean implicature and indirect speech act evaluation; and perceptual grounding in GPT-4 models across six sensory modalities.
Supervisor: Sungeun Lee
Jul–Dec 2020
Undergraduate Researcher
Trained Transformer-based Korean–English translation models with different tokenization strategies. Proposed Grammar Accuracy Evaluation (GAE) to complement BLEU and compared the metrics across four translation directions.
Supervisor: Harksoo Kim