publications

2026

1. CVPR Highlight

   

   Evidential Transformation Network: Turning Pretrained Models into Evidential Models for Post-hoc Uncertainty Estimation

   Yongchan Chun, Chanhee Park, Jeongho Yoon, and 2 more authors

   2026
2. ACL Main

   

   Towards Privacy-Preserving Large Language Model: Text-free Inference Through Alignment and Adaptation

   Jeongho Yoon, Chanhee Park, Yongchan Chun, and 2 more authors

   2026

2025

1. 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
2. EMNLP Main

   

   Benchmark Profiling: Mechanistic Diagnosis of LLM Benchmarks

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

   Nov 2025
3. 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.

2024

1. ACL Findings

   

   Exploring Coding Spot: Understanding Parametric Contributions to LLM Coding Performance

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

   Jul 2024