事实还是虚构？大语言模型能否成为政治真相的可靠标注者？
Fact or Fiction? Can LLMs be Reliable Annotators for Political Truths?
AI总结：
本研究利用开源大语言模型作为政治新闻事实性检测的标注者，通过人类专家验证和LLM评判评估，提供了一种可扩展且稳健的事实核查替代方案。
摘要：
政治错误信息对民主进程构成重大挑战，塑造公众舆论并影响对媒体的信任。人工事实核查方法面临可扩展性和标注者偏见的问题，而机器学习模型则需要大量且成本高昂的标注数据集。本研究探讨使用最先进的大语言模型（LLMs）作为可靠标注者，用于检测新闻文章中的政治事实性。通过使用开源大语言模型，我们创建了一个政治多样化的数据集，并通过LLM生成的标注来标记偏见。这些标注由人类专家验证，并进一步由基于LLM的评判者评估，以评估标注的准确性和可靠性。我们的方法为传统事实核查提供了一种可扩展且稳健的替代方案，增强了媒体透明度和公众信任。
英文摘要：
Political misinformation poses significant challenges to democratic processes, shaping public opinion and trust in media. Manual fact-checking methods face issues of scalability and annotator bias, while machine learning models require large, costly labelled datasets. This study investigates the use of state-of-the-art large language models (LLMs) as reliable annotators for detecting political factuality in news articles. Using open-source LLMs, we create a politically diverse dataset, labelled for bias through LLM-generated annotations. These annotations are validated by human experts and further evaluated by LLM-based judges to assess the accuracy and reliability of the annotations. Our approach offers a scalable and robust alternative to traditional fact-checking, enhancing transparency and public trust in media.
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。
