This study addresses the critical issue of factual inaccuracies in
machine-generated text summaries, an increasingly prevalent issue in
information dissemination. Recognizing the potential of such errors to
compromise information reliability, we investigate the nature of factual
inconsistencies across machine-summarized content. We introduce a prompt-based
classification system that categorizes errors into four distinct types:
misrepresentation, inaccurate quantities or measurements, false attribution,
and fabrication. The participants are tasked with evaluating a corpus of
machine-generated summaries against their original articles. Our methodology
employs qualitative judgements to identify the occurrence of factual
distortions. The results show that our prompt-based approaches are able to
detect the type of errors in the summaries to some extent, although there is
scope for improvement in our classification systems.

本研究旨在解决机器生成的文本摘要中存在的事实错误问题，并研究了这些错误对信息可靠性的潜在影响。我们引入了一种基于提示的分类系统，将错误分为四类：误述、数量或衡量不准确、虚假归属和伪造。通过定性判断，我们使用一组参与者对机器生成的摘要与原文进行了评估，并检测事实失真的发生。结果表明，我们基于提示的方法在一定程度上能够检测到摘要中的错误类型，尽管我们的分类系统还有改进空间。