用对抗反馈提升假新闻检测模型鲁棒性，效果优于主流方法。
RADAR: Retrieval-Augmented Detector with Adversarial Refinement for Robust Fake News Detection
- 通过改写真实文章生成逼真假新闻，配合检索增强检测器。
- 在基准测试中超越强基线模型，检索能力提升贡献最大。
- 适合需要高鲁棒性的假新闻检测场景，尤其对抗未知攻击者。
为有效应对大模型生成的虚假信息传播，我们提出RADAR——一种带有对抗精炼的检索增强检测框架。该方法使用生成器对真实文章进行事实扰动改写，搭配轻量级检测器，利用密集段落检索验证内容真实性。为实现有效协同进化，引入自然语言形式的对抗反馈（VAF），以结构化文本批评指导生成器生成更复杂欺骗性内容，推动检测器持续优化。在假新闻检测基准上，RADAR持续优于多个强基线与通用大模型结合检索的方法。分析显示，检测端的检索带来最大性能提升，而VAF与少量示例演示提供互补增益。此外，RADAR在未见过的外部攻击者生成的假新闻上仍具更好迁移能力，表明其具备超越训练环境的鲁棒性。
原文摘要 · Abstract (English)
To efficiently combat the spread of LLM-generated misinformation, we present RADAR, a Retrieval-Augmented Detector with Adversarial Refinement for robust fake news detection. Our approach employs a generator that rewrites real articles with factual perturbations, paired with a lightweight detector that verifies claims using dense passage retrieval. To enable effective co-evolution, we introduce verbal adversarial feedback (VAF). Rather than relying on scalar rewards, VAF issues structured natural-language critiques; these guide the generator toward more sophisticated evasion attempts, compelling the detector to adapt and improve. On a fake news detection benchmark, RADAR consistently outperforms strong retrieval-augmented trainable baselines, as well as general-purpose LLMs with retrieval. Further analysis shows that detector-side retrieval yields the largest gains, while VAF and few-shot demonstrations provide complementary benefits. RADAR also transfers better to fake news generated by an unseen external attacker, indicating improved robustness beyond the co-evolved training setting.
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