LLM-Assisted Translation for Korean OTT Content Localization: A Literature Review on Productivity, Quality, and Human–AI Collaboration

Abstract

General-text translation is converging toward near-full automation by large language models (LLMs), yet the translation of OTT multimedia content still demands a deliberately designed division of labor between humans and AI; this asymmetry is the starting point of this review. Korea occupies a distinctive position in this transformation: the global success of Korean content has created an asymmetric, outbound-heavy translation market in which linguistically complex Korean-source material must be localized into dozens of languages at speed, while the domestic language-service industry undergoes a structural shift from human translation to machine-translation post-editing. This paper reviews three research streams that have developed largely in isolation, namely LLM translation quality and automatic evaluation, human–AI collaboration and knowledge-worker productivity, and audiovisual translation, and integrates them through an operations-management input–process–output–outcome framework tailored to the Korean OTT context. The review finds that LLM-based post-editing improves draft quality and productivity, but that honorific register, culture-bound expressions, and speaker-relational meaning, all pervasive in Korean dialogue, lie largely outside current LLM competence. Three testable propositions are derived on how Korean-specific linguistic density conditions the productivity–quality trade-off, and a research agenda is proposed with implications for job redesign and capability development in language service providers, OTT platforms, and language-focused higher education. Designing this division of labor is a management problem before it is a technological one, and its answers lie where language expertise and operations management meet.