9 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…
VG-CoT: Towards Trustworthy Visual Reasoning via Grounded Chain-of-Thought
Byeonggeuk Lim, Kyeonghyun Kim, JungMin Yun +1
The advancement of Large Vision-Language Models (LVLMs) requires precise local region-based reasoning that faithfully grounds the model's logic in actual visual evidence. However,…
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…
LLM Agents at the Roundtable: A Multi-Perspective and Dialectical Reasoning Framework for Essay Scoring
Jinhee Jang, Ayoung Moon, Minkyoung Jung +2
The emergence of large language models (LLMs) has brought a new paradigm to automated essay scoring (AES), a long-standing and practical application of natural language processing…
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Although offensive language continually evolves over time, even recent studies using LLMs have predominantly relied on outdated datasets and rarely evaluated the generalization abi…