Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization

TL;DR

Large language models like ChatGPT and GPT-4 show promise in evaluating abstractive summarization but currently lack the consistency and reliability needed to replace human judgment, especially with high-quality summaries.

Contribution

This paper provides an extensive analysis of LLMs as automatic evaluators for abstractive summarization, highlighting their limitations and unreliability compared to human assessments.

Findings

LLMs outperform traditional automatic metrics but are inconsistent.

LLMs struggle to reliably compare closely performing summaries.

Higher-quality summaries lead to lower correlation with human judgments.

Abstract

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.