Human evaluations are often required for abstractive summary evaluations to give fairer judgments. However, they are often time-consuming, costly, inconsistent, and non-reproducible. To overcome these challenges, we explore the potential of using an out-of-the-box LLM (i.e. "gpt-3.5-turbo") for summarization evaluation without manually selecting demonstrations or complex prompt tuning. We compare different evaluation methods, including 2 methods for Likert-scale scoring and 1 method for head-to-head comparisons, to investigate the performance of the LLM as a zero-shot evaluator. We further propose a meta-correlation metric to measure the stability of the LLM's evaluation capability. With extensive experiments, we show that certain prompt formats can produce better results than others. We also bring attention to the LLM's deteriorating evaluation capability with the rising qualities of summaries. In addition, we find that the LLM's evaluation capability also depends on the evaluated dimensions. We discuss the pros and cons of each method, make recommendations, and suggest some future directions for improvement.

本文旨在探讨使用 LLMS（例如“gpt-3.5-turbo”）作为自动评估器来评估摘要的性能，并比较了不同的评估方法和提示格式对其评估能力的影响。作者建议哪些提示格式可以提高LLM的性能，并讨论了LLM的评估能力随摘要质量和评估维度的变化。

大型语言模型是否适合作为抽象概括的评估器？