Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization
This work highlights a critical limitation for researchers and practitioners relying on LLMs for evaluating summarization systems, indicating incremental progress in understanding evaluation methods.
The study investigated the use of large language models (LLMs) like ChatGPT and GPT-4 as automatic evaluators for abstractive summarization, finding that while they outperform traditional metrics, they are inconsistent, dimension-dependent, and unreliable compared to human judges, especially with higher-quality summaries.
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.