Recent Advances in Interactive Machine Translation With Large Language Models

Abstract

This paper explores the role of Large Language Models (LLMs) in revolutionizing interactive Machine Translation (MT), providing a comprehensive analysis across nine innovative research directions. LLMs demonstrate exceptional capabilities in handling complex tasks through advanced text generation and interactive human-machine collaboration, significantly enhancing translation accuracy and efficiency, especially in low-resource language scenarios. This study also outlines potential advancements in LLM applications, emphasizing the integration of domain-specific knowledge and the exploration of model combinations to optimize performance. Future research is suggested to focus on enhancing model adaptability to diverse linguistic environments and refining human-machine interaction frameworks to better serve practical translation needs. The findings contribute to the ongoing discourse on the strategic deployment of MT with LLMs, aiming to direct future developments towards more robust and nuanced language processing solutions.

Keywords

• Machine translation
• large language models
• pre-trained language model
• in-context learning
• post-editing