  In recent years, Large Language Models (LLMs) have gained immense attention
due to their notable emergent capabilities, surpassing those seen in earlier
language models. A particularly intriguing application of LLMs is their role as
evaluators for texts produced by various generative models.
  In this study, we delve into the potential of LLMs as reliable assessors of
factual consistency in summaries generated by text-generation models.
Initially, we introduce an innovative approach for factuality assessment using
LLMs. This entails employing a singular LLM for the entirety of the
question-answering-based factuality scoring process. Following this, we examine
the efficacy of various LLMs in direct factuality scoring, benchmarking them
against traditional measures and human annotations.
  Contrary to initial expectations, our results indicate a lack of significant
correlations between factuality metrics and human evaluations, specifically for
GPT-4 and PaLM-2. Notable correlations were only observed with GPT-3.5 across
two factuality subcategories. These consistent findings across various factual
error categories suggest a fundamental limitation in the current LLMs'
capability to accurately gauge factuality.
  This version presents the information more concisely while maintaining the
main points and findings of the original text.
