DHP Benchmark: Are LLMs Good NLG Evaluators?

• URL: http://arxiv.org/abs/2408.13704v1
• Date: Sun, 25 Aug 2024 02:01:38 GMT
• Title: DHP Benchmark: Are LLMs Good NLG Evaluators?
• Authors: Yicheng Wang, Jiayi Yuan, Yu-Neng Chuang, Zhuoer Wang, Yingchi Liu, Mark Cusick, Param Kulkarni, Zhengping Ji, Yasser Ibrahim, Xia Hu,
• Abstract summary: Large Language Models (LLMs) are increasingly serving as evaluators in Natural Language Generation (NLG) tasks. We propose the Discernment of Hierarchical Perturbation (DHP) benchmarking framework to measure the NLG evaluation capabilities of LLMs. We have re-established six evaluation datasets for this benchmark, covering four NLG tasks: Summarization, Story Completion, Question Answering, and Translation.
• Score: 42.16315294351651
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) are increasingly serving as evaluators in Natural Language Generation (NLG) tasks. However, the capabilities of LLMs in scoring NLG quality remain inadequately explored. Current studies depend on human assessments and simple metrics that fail to capture the discernment of LLMs across diverse NLG tasks. To address this gap, we propose the Discernment of Hierarchical Perturbation (DHP) benchmarking framework, which provides quantitative discernment scores for LLMs utilizing hierarchically perturbed text data and statistical tests to measure the NLG evaluation capabilities of LLMs systematically. We have re-established six evaluation datasets for this benchmark, covering four NLG tasks: Summarization, Story Completion, Question Answering, and Translation. Our comprehensive benchmarking of five major LLM series provides critical insight into their strengths and limitations as NLG evaluators.

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