Multi-Agent Approach to Political Discourse Translation: From Large Language Models to MAGIC-PTF System

Abstract

This research addresses the automated translation of Chinese political discourse into Russian utilizing Large Language Model (LLM) optimization technologies and a multi-agent approach. The study focuses on developing the MAGIC-PTF system, which implements multi-stage text processing through the interaction of four specialized agents. The system's key component is the Style Agent, which ensures stylistic uniformity and terminological accuracy based on a specifically trained LLM. The Translator Agent performs the primary translation work and is responsible for the final text formatting. The Editor Agent conducts multi-level verification and correction of translations, considering linguistic, semantic, and cultural aspects. The Reader Agent analyzes the text from the target audience's perspective, evaluating its reception by native Russian speakers. The methodology integrates LLM optimization technologies and a multi-agent approach, with experimental testing conducted on the fourth volume of "Xi Jinping: The Governance of China" and its official Russian translation. The study includes a comprehensive analysis of system effectiveness using the COMET metric and comparative testing with existing machine translation platforms. The research's scientific novelty lies in developing a methodology for applying LLMs to specialized translation tasks and creating an effective coordination mechanism for intelligent agents in the translation process. Experimental research demonstrated MAGIC-PTF's superiority over traditional machine translation systems in key parameters: terminological accuracy, stylistic consistency, and preservation of culture-specific elements in political discourse. The developed system opens new possibilities for automated translation of political discourse and can be adapted for translating other specialized text types, confirming its significance for modern translation technology development. Of particular value is the system's scalability and adaptability to various language pairs and discourse types, creating prospects for further development in automated specialized text translation. The research findings also contribute to advancing the theory and practice of LLM application in professional translation.