Exploring Strategies for Literary Translation Using Large Language Models

Abstract

Large language models (LLMs) have made significant progress in processing and generating text across multiple languages. However, translating long literary works remains challenging due to the need for consistent character interactions, specialized vocabulary, and coherence across chapters. This paper explores these difficulties and examines methods to achieve decent-quality LLM-based translation of literary texts. Various approaches are considered, including techniques for improving contextual awareness and integrating domain-specific vocabulary to reduce inconsistencies. The analysis highlights both the strengths and limitations of current methods, suggesting that targeted context management and fine-tuning strategies have the potential to improve translation accuracy in certain cases. These insights contribute to the development of more effective translation systems for literary texts and multilingual content.