Who Has the Final Word? Designing Multi-Agent Collaborative Framework for Professional Translators

Abstract: Recent advances in LLM based translation have led to renewed interest in fully automated systems, yet professional translators remain essential in high stakes domains where decisions about accuracy, terminology, style, and audience cannot be safely automated. Current tools are typically single shot generators or single-agent self-refiners, offering limited support for translator multidimensional decision making process and providing little structured leverage for translator input. We present CHORUS, a human-AI multiagent collaborative translation framework grounded in the Multidimensional Quality Metrics (MQM) framework, which decomposes quality dimensions into specialized agents and integrates their feedback into an iterative refinement loop controlled by the translator. A six-user preliminary study with professional translators found that CHORUS consistently outperforms zero-shot and single-agent baselines, showing that MQM-aligned multi-agent collaboration better supports professional translation workflows than autonomous generation.