This paper describes the IUST NLP Lab submission to the Prompting Large Language Models as Explainable Metrics Shared Task at the Eval4NLP 2023 Workshop on Evaluation & Comparison of NLP Systems. We have proposed a zero-shot prompt-based strategy for explainable evaluation of the summarization task using Large Language Models (LLMs). The conducted experiments demonstrate the promising potential of LLMs as evaluation metrics in Natural Language Processing (NLP), particularly in the field of summarization. Both few-shot and zero-shot approaches are employed in these experiments. The performance of our best provided prompts achieved a Kendall correlation of 0.477 with human evaluations in the text summarization task on the test data. Code and results are publicly available on GitHub.

Last month, Mark Clattenburg, who is generally regarded as one of the finest referees in professional soccer, left England's Premier League for a better-paying position in Saudi Arabia. Plenty of famous players have chosen riches over prestige and joined less established leagues in Asia and the Middle East. But this was one of the first times that a referee of Clattenburg's stature and prominence had left in his prime. Just last year, he was selected to referee two of the most important matches on Earth: the finals of the Champions League, in May, and then the European Championship, two months later--plum gigs that testified to his skill. To commemorate the occasion, he had the feat tattooed on his arm--a testament to his knack for preening.