Yongchan Chun

I’m a Master’s candidate at the NLP&AI Lab at Korea University, advised by Heuiseok Lim. My research covers several areas, including representation learning, retrieval-augmented generation (RAG), uncertainty estimation, and automatic speech recognition (ASR). Working across different modalities has given me a broad perspective on solving complex problems. Over time, my various experiences have converged toward one key challenge—the trustworthiness of AI. Currently, my work focuses on developing trustworthy AI systems for large pretrained models.

selected publications

ACL Findings

Enhancing Automatic Term Extraction with Large Language Models via Syntactic Retrieval

Yongchan Chun, Minhyuk Kim, Dongjun Kim, and 2 more authors

In Findings of the Association for Computational Linguistics: ACL 2025 , Jul 2025

Abs

Automatic Term Extraction (ATE) identifies domain-specific expressions that are crucial for downstream tasks such as machine translation and information retrieval. Although large language models (LLMs) have significantly advanced various NLP tasks, their potential for ATE has scarcely been examined. We propose a retrieval-based prompting strategy that, in the few-shot setting, selects demonstrations according to syntactic rather than semantic similarity. This syntactic retrieval method is domain-agnostic and provides more reliable guidance for capturing term boundaries. We evaluate the approach in both in-domain and cross-domain settings, analyzing how lexical overlap between the query sentence and its retrieved examples affects performance. Experiments on three specialized ATE benchmarks show that syntactic retrieval improves F1-score. These findings highlight the importance of syntactic cues when adapting LLMs to terminology-extraction tasks.
EMNLP Findings

KoLEG: On-the-Fly Korean Legal Knowledge Editing with Continuous Retrieval

Jaehyung Seo, Dahyun Jung, Jaewook Lee, and 5 more authors

In Findings of the Association for Computational Linguistics: EMNLP 2025 , Nov 2025
EMNLP Main

Benchmark Profiling: Mechanistic Diagnosis of LLM Benchmarks

Dongjun Kim, Gyuho Shim, Yongchan Chun, and 3 more authors

Nov 2025
Preprint

Exploring Coding Spot: Understanding Parametric Contributions to LLM Coding Performance

Dongjun Kim, Minhyuk Kim, YongChan Chun, and 2 more authors

Nov 2024