从生成到检测：基于话语驱动场景的LLM生成假新闻探索
From Generation to Detection: Exploration of Discourse Driven Scenario based LLM Generated Fake News
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中文总结 AI 辅助
本研究系统评估了LLM在四种生成场景下生成和检测假新闻的能力，发现生成策略显著影响可检测性，且细化提示未能提升检测性能。
中文摘要 AI 辅助
在本研究中，我们考察了现代LLM在四种操纵场景下的受控设置中如何生成和检测假新闻。这些场景包括开放式生成、改写、操纵提示以及基于新闻话语框架的属性提示。首先，使用七个广泛采用的模型，我们创建了一个合成假新闻语料库，包含14000篇跨这四个场景生成的新闻文章。然后，我们分析了其语言特性，以评估模型生成的新闻在结构和语义上与真实新闻的相似程度。最后，为了评估检测性能，我们进行了实验，每个模型对生成的假新闻进行判断，从基础检测提示开始，并通过迭代细化过程开发改进的提示，该过程从真实-虚假配对中提取误导性模式。我们的结果揭示了不同模型在生成和检测错误信息方面的显著差异，表明生成策略强烈影响可检测性，并显示细化的提示并未改善且常常损害检测性能。因此，本研究对LLM在典型生成场景下检测LLM生成的假新闻的能力进行了系统评估。
英文摘要
In this study, we examine how modern LLMs generate and detect fake news under controlled settings across four manipulation scenarios. These are open-ended generation, rewriting, manipulation prompts and attribute based prompts grounded in the journalistic discourse framework. Firstly, using seven widely adapted models, we created a synthetic fake news corpus with 14000 generated articles across these four scenarios. Then we analyzed its linguistic properties to assess how closely model-generated news resembles real news structurally and semantically. Finally, to evaluate detection performance, we conducted experiments where each model judges generated fake news, starting with a basic detection prompt and improved prompts developed through an iterative refinement process that extracts misleading patterns from real-fake pairs. Our results revealed substantial variation across models in both generating and detecting misinformation, demonstrated that the generation strategy strongly influences detectability, and show that the refined prompt does not improve and often harms detection performance. Therefore, the study provides a systematic assessment of LLMs detection capability of LLMs generated fake news across typical generation scenarios.
发表机构
- Ankara University（安卡拉大学）
机构由 AI 辅助整理，请以论文原文为准。
