← Search

Dahyun Jung

7 accepted papers

2025

CoME: An Unlearning-based Approach to Conflict-free Model Editing

NAACL 2025long

Large language models (LLMs) often retain outdated or incorrect information from pre-training, which undermines their reliability. While model editing methods have been developed to address such errors without full re-training, they frequently suffer from knowledge conflicts, where outdated informat…

2025

FLEX: A Benchmark for Evaluating Robustness of Fairness in Large Language Models

NAACL 2025findings

Recent advancements in Large Language Models (LLMs) have significantly enhanced interactions between users and models. These advancements concurrently underscore the need for rigorous safety evaluations due to the manifestation of social biases, which can lead to harmful societal impacts. Despite th…

2025

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

EMNLP 2025

Korean legal knowledge is subject to frequent temporal updates driven by societal needs and government policies. Even minor modifications to legal provisions can have significant consequences, yet continuously retraining large language models (LLMs) to incorporate such updates is resource-intensive

2024

Detecting Critical Errors Considering Cross-Cultural Factors in English-Korean Translation

COLING 2024main

Recent machine translation (MT) systems have overcome language barriers for a wide range of users, yet they still carry the risk of critical meaning deviation. Critical error detection (CED) is a task that identifies an inherent risk of catastrophic meaning distortions in the machine translation out…

2024

Leveraging Pre-existing Resources for Data-Efficient Counter-Narrative Generation in Korean

COLING 2024main

Counter-narrative generation, i.e., the generation of fact-based responses to hate speech with the aim of correcting discriminatory beliefs, has been demonstrated to be an effective method to combat hate speech. However, its effectiveness is limited by the resource-intensive nature of dataset constr…

2024

Towards Precise Localization of Critical Errors in Machine Translation

ACL 2024findings

The advent of large language models has experienced a remarkable improvement in the field of machine translation. However, machine translation is still vulnerable to critical meaning deviations, which may incur catastrophic issues in social or ethical contexts. In particular, existing critical error…