LLM Based Machine Translation

Large language model (LLM)-based machine translation leverages the power of LLMs to improve the accuracy and efficiency of translating text between languages. Current research focuses on addressing challenges like verbose outputs, error correction (especially in code translation), and handling culturally specific terms, often employing techniques like multi-agent systems and fine-tuning with various data augmentation strategies. This approach offers significant potential for improving translation quality, particularly in low-resource language settings and specialized domains, and is driving innovation in both the theoretical understanding and practical applications of machine translation.