8 papers
FairQE: Multi-Agent Framework for Mitigating Gender Bias in Translation Quality Estimation
Jinhee Jang, Juhwan Choi, Dongjin Lee +2
Quality Estimation (QE) aims to assess machine translation quality without reference translations, but recent studies have shown that existing QE models exhibit systematic gender b…
Steering LLMs toward Korean Local Speech: Iterative Refinement Framework for Faithful Dialect Translation
Keunhyeung Park, Seunguk Yu, Youngbin Kim
Standard-to-dialect machine translation remains challenging due to a persistent dialect gap in large language models and evaluation distortions inherent in n-gram metrics, which fa…
Medal Matters: Probing LLMs' Failure Cases Through Olympic Rankings
Juhwan Choi, Seunguk Yu, JungMin Yun +1
Large language models (LLMs) have achieved remarkable success in natural language processing tasks, yet their internal knowledge structures remain poorly understood. This study exa…
From Ground Trust to Truth: Disparities in Offensive Language Judgments on Contemporary Korean Political Discourse
Seunguk Yu, Jungmin Yun, Jinhee Jang +1
Although offensive language continually evolves over time, even recent studies using LLMs have predominantly relied on outdated datasets and rarely evaluated the generalization abi…
VolDoGer: LLM-assisted Datasets for Domain Generalization in Vision-Language Tasks
Juhwan Choi, Junehyoung Kwon, JungMin Yun +2
Domain generalizability is a crucial aspect of a deep learning model since it determines the capability of the model to perform well on data from unseen domains. However, research…
Making Sense of Korean Sentences: A Comprehensive Evaluation of LLMs through KoSEnd Dataset
Seunguk Yu, Kyeonghyun Kim, Jungmin Yun +1
Although LLMs have made significant progress in various languages, there are still concerns about their effectiveness with low-resource agglutinative languages compared to language…