AI RESEARCH
Steering LLMs toward Korean Local Speech: Iterative Refinement Framework for Faithful Dialect Translation
arXiv CS.CL
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ArXi:2511.06680v2 Announce Type: replace 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 favor source copying over authentic dialect translation. In this paper, we propose the dialect refinement (DIA-REFINE) framework, which guides LLMs toward faithful target dialect outputs through an iterative loop of translation, verification, and feedback using external dialect classifiers. To address the limitations of n-gram-based metrics, we.