GEMBA-MQM: Detecting Translation Quality Error Spans with GPT-4
Tom Kocmi Christian Federmann
This paper introduces GEMBA-MQM, a GPT-based evaluation metric designed to
detect translation quality errors, specifically for the quality estimation
setting without the need for human reference translations. Based on the power
of large language models (LLM), GEMBA-MQM employs a fixed three-shot prompting
technique, querying the GPT-4 model to mark error quality spans. Compared to
previous works, our method has language-agnostic prompts, thus avoiding the
need for manual prompt preparation for new languages.
  While preliminary results indicate that GEMBA-MQM achieves state-of-the-art
accuracy for system ranking, we advise caution when using it in academic works
to demonstrate improvements over other methods due to its dependence on the
proprietary, black-box GPT model.