事实性与欺骗性在德语中究竟有多纠缠？
How Entangled is Factuality and Deception in German?
AI总结：
本文基于信念欺骗框架研究德语文本中事实性与欺骗性的关系，发现计算模型在欺骗检测上仅相当于随机猜测，且欺骗性会削弱基于自然语言推理的事实核查表现，而大语言模型提示方法受此影响较小。
摘要：
“地球是平的”这一陈述在事实上不准确，但如果有人真心相信并为之辩护，它就不是欺骗性的。关于欺骗检测与事实核查的研究常常将事实准确性与陈述的真实性混为一谈。这一假设使得（a）研究二者之间细微的区别与相互作用，以及（b）衡量它们对下游任务的影响变得困难。基于信念的欺骗框架通过将文本定义为欺骗性的——即当人们所说与其真正相信的内容不一致时——来解耦这些属性。在本研究中，我们评估假定的欺骗模式是否能推广到德语文本。我们使用一个既有的基于信念的论证语料库，测试计算模型在检测欺骗方面的有效性。最后，我们衡量欺骗对事实核查这一下游任务的影响，并探究该属性是否会混淆验证模型。令人惊讶的是，我们的分析发现其与既有的欺骗线索之间没有相关性。先前的工作声称计算模型在欺骗检测准确率上可以超越人类，然而我们的实验表明，无论是传统模型还是最先进的模型在该任务上都表现挣扎，其表现并不优于随机猜测。在事实核查方面，我们发现基于自然语言推理的验证在非事实性和欺骗性内容上表现更差，而通过提示大语言模型执行相同任务则对这些属性不那么敏感。
英文摘要：
The statement "The earth is flat" is factually inaccurate, but if someone truly believes and argues in its favor, it is not deceptive. Research on deception detection and fact checking often conflates factual accuracy with the truthfulness of statements. This assumption makes it difficult to (a) study subtle distinctions and interactions between the two and (b) gauge their effects on downstream tasks. The belief-based deception framework disentangles these properties by defining texts as deceptive when there is a mismatch between what people say and what they truly believe. In this study, we assess if presumed patterns of deception generalize to German language texts. We test the effectiveness of computational models in detecting deception using an established corpus of belief-based argumentation. Finally, we gauge the impact of deception on the downstream task of fact checking and explore if this property confounds verification models. Surprisingly, our analysis finds no correlation with established cues of deception. Previous work claimed that computational models can outperform humans in deception detection accuracy, however, our experiments show that both traditional and state-of-the-art models struggle with the task, performing no better than random guessing. For fact checking, we find that Natural Language Inference-based verification performs worse on non-factual and deceptive content, while prompting Large Language Models for the same task is less sensitive to these properties.
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。
