10 papers
Detecting and Guiding LLM-Generated Korean Poetry with Interpretable Form-level Features
Keunhyeung Park, Seunguk Yu, YoungBin Kim
LLMs often struggle with modern Korean poetry, producing outputs that resemble "line-broken prose." We address two coupled tasks: detecting whether a Korean poem is human- or LLM-a…
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…
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Although offensive language continually evolves over time, even recent studies using LLMs have predominantly relied on outdated datasets and rarely evaluated the generalization abi…
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…
Delving into Multilingual Ethical Bias: The MSQAD with Statistical Hypothesis Tests for Large Language Models
Seunguk Yu, Juhwan Choi, Youngbin Kim
Despite the recent strides in large language models, studies have underscored the existence of social biases within these systems. In this paper, we delve into the validation and c…
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…
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…
UniGen: Universal Domain Generalization for Sentiment Classification via Zero-shot Dataset Generation
Juhwan Choi, Yeonghwa Kim, Seunguk Yu +2
Although pre-trained language models have exhibited great flexibility and versatility with prompt-based few-shot learning, they suffer from the extensive parameter size and limited…
Don't be a Fool: Pooling Strategies in Offensive Language Detection from User-Intended Adversarial Attacks
Seunguk Yu, Juhwan Choi, Youngbin Kim
Offensive language detection is an important task for filtering out abusive expressions and improving online user experiences. However, malicious users often attempt to avoid filte…