  With the recent undeniable advancement in reasoning abilities in large
language models (LLMs) like ChatGPT and GPT-4, there is a growing trend for
using LLMs on various tasks. One area where LLMs can be employed is as an
alternative evaluation metric for complex generative tasks, which generally
demands expensive human judges to complement the traditional automatic metrics
for various evaluation dimensions such as fluency and consistency. In this
work, we conduct extensive analysis to investigate the stability and
reliability of LLMs as automatic evaluators for abstractive summarization. We
found that while ChatGPT and GPT-4 outperform the commonly used automatic
metrics, they are not ready as human replacements due to significant
limitations. That is, LLM evaluators rate each candidate system inconsistently
and are dimension-dependent. They also struggle to compare candidates with
close performance and become more unreliable with higher-quality summaries by
obtaining a lower correlation with humans. In other words, with better
abstractive summarization systems being introduced at a fast pace, LLMs may
result in misleading and unreliable evaluations.
