Papers by Dahyun Jung
StepKE: Stepwise Knowledge Editing for Multi-Hop Question Answering (2025.findings-emnlp)
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| Challenge: | Existing knowledge editing methods overlook interplay with pre-existing knowledge, leading to inconsistent edit propagation. |
| Approach: | stepKE integrates edited and existing knowledge for coherent multi-hop reasoning . stepKE decomposes multi-step questions into sequential single-hop sub-questions . |
| Outcome: | Experiments show that StepKE generates more accurate and consistent responses than baselines. |
EASE: Entity-Aware Sub-table Generation for Real-world Multi-table QA (2026.acl-long)
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Myunghoon Kang, Dahyun Jung, Suhyune Son, Seonmin Koo, Changwoo Chun, Daniel Rim, Haeyoung Kwon, Yuna Hur, Heuiseok Lim
| Challenge: | Table-based question answering (table QA) is a powerful tool for analyzing large language models. |
| Approach: | They propose to use noisy multi-table sets to generate sub-tables for table-based question answering. |
| Outcome: | The proposed framework efficiently filters out irrelevant information while incorporating pertinent table values. |
CoME: An Unlearning-based Approach to Conflict-free Model Editing (2025.naacl-long)
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| Challenge: | Large language models (LLMs) often retain outdated or incorrect information from pre-training, which undermines their reliability. |
| Approach: | They propose a conflict-free model editing framework that selectively removes outdated knowledge from LLMs to improve their accuracy and reliability. |
| Outcome: | The proposed framework improves both editing accuracy and model reliability when applied to existing editing methods. |
KoLEG: On-the-Fly Korean Legal Knowledge Editing with Continuous Retrieval (2025.findings-emnlp)
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Jaehyung Seo, Dahyun Jung, Jaewook Lee, Yongchan Chun, Dongjun Kim, Hwijung Ryu, Donghoon Shin, Heuiseok Lim
| Challenge: | a recent study shows that Korean legal knowledge is subject to frequent temporal updates driven by societal needs and government policies. |
| Approach: | They propose a Korean Legal knowledge editing framework enhanced with continuous retrieval . they employ an Editing-Aware Learning Strategy and a LawEdit Retriever . |
| Outcome: | a new framework outperforms existing methods for updating legal knowledge in Korean . it maintains robust performance in sequential editing and is qualitatively validated by legal experts. |
Exploring Inherent Biases in LLMs within Korean Social Context: A Comparative Analysis of ChatGPT and GPT-4 (2024.naacl-srw)
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| Challenge: | Large Language Models (LLMs) have been criticized for perpetuating stereotypes against diverse groups based on race, sexual orientation, and other attributes. |
| Approach: | They devised a set of prompts that reflect major societal issues in Korea and assign varied personas to both ChatGPT and GPT-4 to assess the toxicity of the generated sentences. |
| Outcome: | The proposed model produces twice the level of toxic content as ChatGPT and GPT-4 under certain conditions. |
FLEX: A Benchmark for Evaluating Robustness of Fairness in Large Language Models (2025.findings-naacl)
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| Challenge: | Existing safety evaluations may overlook the inherent weaknesses of Large Language Models, despite their benefits. |
| Approach: | They propose a benchmark to evaluate the robustness of Large Language Models under extreme conditions. |
| Outcome: | The proposed approach evaluates the fairness of large language models under extreme conditions. |
Towards Precise Localization of Critical Errors in Machine Translation (2024.findings-acl)
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| Challenge: | Existing critical error detection focuses on identifying sentence-level errors, leaving the precise localization of such errors unaddressed. |
| Approach: | They propose a task to detect critical errors at a fine-grained level in machine translation sentences. |
| Outcome: | The proposed method outperforms existing methods and LLMs in English to Korean translations. |
Towards Scalable Lifelong Knowledge Editing with Selective Knowledge Suppression (2026.acl-long)
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| Challenge: | Existing methods to modify knowledge are limited due to high training costs and lack stability during sequential edits due to catastrophic forgetting. |
| Approach: | They propose a framework to modify specific knowledge of large language models without retraining the entire model. |
| Outcome: | Extensive experiments on ZSRE, Counterfact, and RIPE show that LightEdit outperforms existing lifelong knowledge editing methods. |
Explainable CED: A Dataset for Explainable Critical Error Detection in Machine Translation (2024.naacl-srw)
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| Challenge: | Existing studies of critical error detection lack content addressing the causes of catastrophic errors. |
| Approach: | They propose a dataset that introduces the attributes of error explanation and correction regarding critical errors. |
| Outcome: | The proposed dataset reduces time costs and mitigates human annotation bias. |