SIFiD：基于大语言模型重新评估摘要事实不一致性检测
SIFiD: Reassess Summary Factual Inconsistency Detection with LLM
AI总结：
本研究针对大语言模型在摘要事实不一致性检测中表现不佳的问题，重新评估GPT-3.5与GPT-4的检测性能，并提出SIFiD框架，通过自然语言推理或语义相似度识别文档关键句以提升检测效果。
摘要：
在摘要生成任务中，确保摘要与原文的事实一致性至关重要，因此学界已投入大量研究开展不一致性检测工作。随着大语言模型（LLM）的出现，近期研究开始利用其先进的语言理解能力进行不一致性检测，但早期尝试显示LLM表现不及传统模型，原因在于其指令遵循能力有限，且缺乏有效的检测方法。本研究重新评估基于LLM的摘要不一致性检测任务，对比了GPT-3.5与GPT-4的性能。为推动相关研究发展，我们提出SIFiD（基于过滤文档的摘要不一致性检测）框架，通过自然语言推理或度量摘要与文档的语义相似度来识别文档中的关键句子。
英文摘要：
Ensuring factual consistency between the summary and the original document is paramount in summarization tasks. Consequently, considerable effort has been dedicated to detecting inconsistencies. With the advent of Large Language Models (LLMs), recent studies have begun to leverage their advanced language understanding capabilities for inconsistency detection. However, early attempts have shown that LLMs underperform traditional models due to their limited ability to follow instructions and the absence of an effective detection methodology. In this study, we reassess summary inconsistency detection with LLMs, comparing the performances of GPT-3.5 and GPT-4. To advance research in LLM-based inconsistency detection, we propose SIFiD (Summary Inconsistency Detection with Filtered Document) that identify key sentences within documents by either employing natural language inference or measuring semantic similarity between summaries and documents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
