Summary of M-mad: Multidimensional Multi-agent Debate For Advanced Machine Translation Evaluation, by Zhaopeng Feng et al.
M-MAD: Multidimensional Multi-Agent Debate for Advanced Machine Translation Evaluation
by Zhaopeng Feng, Jiayuan Su, Jiamei Zheng, Jiahan Ren, Yan Zhang, Jian Wu, Hongwei Wang, Zuozhu Liu
First submitted to arxiv on: 28 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes Multidimensional Multi-Agent Debate (M-MAD), a novel framework for evaluating machine translation (MT) using large language models (LLMs). The authors argue that current LLM-as-a-judge methods fall short in MT evaluation, and M-MAD aims to address this gap. By decoupling heuristic MQM criteria into distinct dimensions, employing multi-agent debates, and synthesizing dimension-specific results, M-MAD achieves significant advancements over existing approaches. Experimental results show that M-MAD outperforms LLM-as-a-judge methods and competes with state-of-the-art reference-based automatic metrics, even when using a suboptimal model like GPT-4o mini. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary M-MAD is a new way to use large language models to evaluate machine translation. The current method for evaluating MT isn’t very good, so the authors created M-MAD to fix this problem. It works by breaking down evaluation criteria into smaller parts, having multiple LLMs discuss and agree on a score, and then combining those scores to get a final result. This approach is much better than what’s currently being used. |
Keywords
» Artificial intelligence » Gpt » Translation
