从欺骗到检测：大型语言模型在假新闻中的双重角色
From Deception to Detection: The Dual Roles of Large Language Models in Fake News
AI总结：
本文探讨了大型语言模型（LLMs）在假新闻中的双重角色，分析了七个模型生成带偏见假新闻的能力及检测假新闻的表现，发现部分模型能轻易生成假新闻且更难被检测，而大模型检测能力更强，且LLM生成的解释有助于用户识别假新闻。
摘要：
假新闻对信息生态系统的完整性和公众信任构成了重大威胁。大型语言模型（LLMs）的出现为改变打击假新闻的斗争带来了巨大希望。总体而言，LLMs在这场斗争中代表了一把双刃剑。一个主要担忧是，LLMs可以轻易地被用于大规模制造和传播误导性信息。这引发了一个紧迫的问题：LLMs能否轻易生成带有偏见的假新闻？是否所有LLMs都具备这种能力？相反，得益于其广泛的世界知识和强大的推理能力，LLMs为对抗假新闻提供了宝贵的前景。这引出了其他关键问题：我们能否使用LLMs检测假新闻，并且它们是否优于典型的检测模型？在本文中，我们旨在通过探索各种LLMs的性能来解决这些关键问题。我们的目标是探索各种LLMs在有效打击假新闻方面的能力，这标志着首次对七个此类模型进行分析的调查。我们的结果显示，虽然一些模型严格遵守安全协议，拒绝生成带有偏见或误导性的内容，但其他模型可以轻易地在各种偏见范围内制造假新闻。此外，我们的结果表明，较大的模型通常展现出更优越的检测能力，并且LLM生成的假新闻比人类撰写的假新闻更难被检测到。最后，我们的发现证明，用户可以从LLM生成的解释中受益，以识别假新闻。
英文摘要：
Fake news poses a significant threat to the integrity of information ecosystems and public trust. The advent of Large Language Models (LLMs) holds considerable promise for transforming the battle against fake news. Generally, LLMs represent a double-edged sword in this struggle. One major concern is that LLMs can be readily used to craft and disseminate misleading information on a large scale. This raises the pressing questions: Can LLMs easily generate biased fake news? Do all LLMs have this capability? Conversely, LLMs offer valuable prospects for countering fake news, thanks to their extensive knowledge of the world and robust reasoning capabilities. This leads to other critical inquiries: Can we use LLMs to detect fake news, and do they outperform typical detection models? In this paper, we aim to address these pivotal questions by exploring the performance of various LLMs. Our objective is to explore the capability of various LLMs in effectively combating fake news, marking this as the first investigation to analyze seven such models. Our results reveal that while some models adhere strictly to safety protocols, refusing to generate biased or misleading content, other models can readily produce fake news across a spectrum of biases. Additionally, our results show that larger models generally exhibit superior detection abilities and that LLM-generated fake news are less likely to be detected than human-written ones. Finally, our findings demonstrate that users can benefit from LLM-generated explanations in identifying fake news.
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。
