M-MAD: Multidimensional Multi-Agent Debate for Advanced Machine Translation Evaluation

• URL: http://arxiv.org/abs/2412.20127v3
• Date: Thu, 20 Feb 2025 12:55:22 GMT
• Title: M-MAD: Multidimensional Multi-Agent Debate for Advanced Machine Translation Evaluation
• Authors: Zhaopeng Feng, Jiayuan Su, Jiamei Zheng, Jiahan Ren, Yan Zhang, Jian Wu, Hongwei Wang, Zuozhu Liu,
• Abstract summary: Multidimensional Multi-Agent Debate (M-MAD) is a systematic LLM-based multi-agent framework for advanced machine translation evaluation. Our findings demonstrate that M-MAD achieves significant advancements by (1) decoupling MQM criteria into distinct evaluation dimensions for fine-grained assessments. Comprehensive experiments show that M-MAD not only outperforms all existing LLM-as-a-judge methods but also competes with state-of-the-art reference-based automatic metrics.
• Score: 12.042804590050089
• License:
• Abstract: Recent advancements in large language models (LLMs) have given rise to the LLM-as-a-judge paradigm, showcasing their potential to deliver human-like judgments. However, in the field of machine translation (MT) evaluation, current LLM-as-a-judge methods fall short of learned automatic metrics. In this paper, we propose Multidimensional Multi-Agent Debate (M-MAD), a systematic LLM-based multi-agent framework for advanced LLM-as-a-judge MT evaluation. Our findings demonstrate that M-MAD achieves significant advancements by (1) decoupling heuristic MQM criteria into distinct evaluation dimensions for fine-grained assessments; (2) employing multi-agent debates to harness the collaborative reasoning capabilities of LLMs; (3) synthesizing dimension-specific results into a final evaluation judgment to ensure robust and reliable outcomes. Comprehensive experiments show that M-MAD not only outperforms all existing LLM-as-a-judge methods but also competes with state-of-the-art reference-based automatic metrics, even when powered by a suboptimal model like GPT-4o mini. Detailed ablations and analysis highlight the superiority of our framework design, offering a fresh perspective for LLM-as-a-judge paradigm. Our code and data are publicly available at https://github.com/SU-JIAYUAN/M-MAD.

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