# Exploring Prompting Large Language Models as Explainable Metrics

arXiv: https://arxiv.org/abs/2311.11552 · HTML: https://mlsanity.com/paper/2311.11552/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 13 references that are in the map.
- First posted on arXiv 20 Nov 2023.

## Builds on (most cited first, 7 of 7)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [SummEval: Re-evaluating Summarization Evaluation](https://mlsanity.com/paper/2007.12626/) (2020)
- [Is ChatGPT a Good NLG Evaluator? A Preliminary Study](https://mlsanity.com/paper/2303.04048/) (2023)
- [Large Language Models Are State-of-the-Art Evaluators of Translation Quality](https://mlsanity.com/paper/2302.14520/) (2023)
- [The Eval4NLP 2023 Shared Task on Prompting Large Language Models as Explainable Metrics](https://mlsanity.com/paper/2310.19792/) (2023)

## Built on by (most cited first, 4 of 4)

- [Large Language Models Are Active Critics in NLG Evaluation](https://mlsanity.com/paper/2410.10724/) (2024)
- [A Two-dimensional Zero-shot Dialogue State Tracking Evaluation Method using GPT-4](https://mlsanity.com/paper/2406.11651/) (2024)
- [Enhancing News Summarization with ELearnFit through Efficient In-Context Learning and Efficient Fine-Tuning](https://mlsanity.com/paper/2405.02710/) (2024)
- [LLM-ReSum: A Framework for LLM Reflective Summarization through Self-Evaluation](https://mlsanity.com/paper/2604.25665/) (2026)

## Abstract (arXiv)

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.