Top Language AI Researchers Propose New Way to Auto-Evaluate Machine Translation

In March 2023, Tom Kocmi and Christian Federmann from Microsoft demonstrated that large language models (LLM) can be prompted to assess the quality of machine translation (MT), achieving state-of-the-art (SOTA) performance in assessing system-level quality.<br><br>However, their focus was primarily on score prediction (i.e. predicting a numerical value for quality) without considering the use of any annotated data u2014 either through in-context learning or fine-tuning.<br><br>More recently, Serge Gladkoff and Gleb Erofeev from Logrus Global, along with Lifeng Han and Goran Nenadic from the University of Manc

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