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

Are Large Language Models Reliable Judges? A Study on the Factuality Evaluation Capabilities of LLMs