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        <title>Exploring Prompting Large Language Models as Explainable Metrics</title>
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        <namePart type="given">Ghazaleh</namePart>
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            <namePart type="given">Daniel</namePart>
            <namePart type="family">Deutsch</namePart>
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    <abstract>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 &amp; 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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.eval4nlp-1.18</identifier>
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%0 Conference Proceedings
%T Exploring Prompting Large Language Models as Explainable Metrics
%A Mahmoudi, Ghazaleh
%Y Deutsch, Daniel
%Y Dror, Rotem
%Y Eger, Steffen
%Y Gao, Yang
%Y Leiter, Christoph
%Y Opitz, Juri
%Y Rücklé, Andreas
%S Proceedings of the 4th Workshop on Evaluation and Comparison of NLP Systems
%D 2023
%8 November
%I Association for Computational Linguistics
%C Bali, Indonesia
%F mahmoudi-2023-exploring
%X 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.
%R 10.18653/v1/2023.eval4nlp-1.18
%U https://aclanthology.org/2023.eval4nlp-1.18
%U https://doi.org/10.18653/v1/2023.eval4nlp-1.18
%P 219-227

Markdown (Informal)

[Exploring Prompting Large Language Models as Explainable Metrics](https://aclanthology.org/2023.eval4nlp-1.18) (Mahmoudi, Eval4NLP-WS 2023)

• Exploring Prompting Large Language Models as Explainable Metrics (Mahmoudi, Eval4NLP-WS 2023)

ACL

• Ghazaleh Mahmoudi. 2023. Exploring Prompting Large Language Models as Explainable Metrics. In Proceedings of the 4th Workshop on Evaluation and Comparison of NLP Systems, pages 219–227, Bali, Indonesia. Association for Computational Linguistics.