A Dual-Perspective NLG Meta-Evaluation Framework with Automatic Benchmark and Better Interpretability
Xinyu Hu, Mingqi Gao, Li Lin, Zhenghan Yu, Xiaojun Wan
Abstract
In NLG meta-evaluation, evaluation metrics are typically assessed based on their consistency with humans. However, we identify some limitations in traditional NLG meta-evaluation approaches, such as issues in handling human ratings and ambiguous selections of correlation measures, which undermine the effectiveness of meta-evaluation. In this work, we propose a dual-perspective NLG meta-evaluation framework that focuses on different evaluation capabilities, thereby providing better interpretability. In addition, we introduce a method of automatically constructing the corresponding benchmarks without requiring new human annotations. Furthermore, we conduct experiments with 16 representative LLMs as the evaluators based on our proposed framework, comprehensively analyzing their evaluation performance from different perspectives.
BibTeX
@inproceedings{hu-etal-2025-dual,
title = "A Dual-Perspective {NLG} Meta-Evaluation Framework with Automatic Benchmark and Better Interpretability",
author = "Hu, Xinyu and
Gao, Mingqi and
Lin, Li and
Yu, Zhenghan and
Wan, Xiaojun",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1327/",
doi = "10.18653/v1/2025.acl-long.1327",
pages = "27372--27395",
ISBN = "979-8-89176-251-0"
}