Rapid LLM advancements heighten fake news risks by enabling the automatic generation of increasingly sophisticated misinformation. Previous detection methods, including fine-tuned small models or LLM-based detectors, often struggle with its dynamically evolving nature. In this work, we propose a novel framework called the Symbolic Adversarial Learning Framework (SALF), which implements an adversarial training paradigm by an agent symbolic learning optimization process, rather than relying on numerical updates. SALF introduces a paradigm where the generation agent crafts deceptive narratives, and the detection agent uses structured debates to identify logical and factual flaws for detection, and they iteratively refine themselves through such adversarial interactions. Unlike traditional neural updates, we represent agents using agent symbolic learning, where learnable weights are defined by agent prompts, and simulate back-propagation and gradient descent by operating on natural language representations of weights, loss, and gradients. Experiments on two multilingual benchmark datasets demonstrate SALF's effectiveness, showing it generates sophisticated fake news that degrades state-of-the-art detection performance by up to 53.4% in Chinese and 34.2% in English on average. SALF also refines detectors, improving detection of refined content by up to 7.7%. We hope our work inspires further exploration into more robust, adaptable fake news detection systems.

翻译：大型语言模型（LLM）的快速发展通过实现日益复杂的虚假信息的自动生成，加剧了虚假新闻的风险。以往的检测方法，包括微调的小型模型或基于LLM的检测器，常常难以应对其动态演变的特性。在本工作中，我们提出了一种称为符号对抗学习框架（SALF）的新颖框架，该框架通过智能体符号学习优化过程实现对抗训练范式，而非依赖于数值更新。SALF引入了一种范式，其中生成智能体精心制作欺骗性叙事，而检测智能体则利用结构化辩论来识别逻辑和事实缺陷以进行检测，它们通过此类对抗性交互迭代地自我完善。与传统的神经更新不同，我们使用智能体符号学习来表示智能体，其中可学习的权重由智能体提示定义，并通过操作权重的自然语言表示、损失和梯度来模拟反向传播和梯度下降。在两个多语言基准数据集上的实验证明了SALF的有效性，表明其生成的复杂虚假新闻使最先进的检测性能平均下降高达53.4%（中文）和34.2%（英文）。SALF还能优化检测器，将针对优化内容的检测性能提升高达7.7%。我们希望我们的工作能激发对更鲁棒、适应性更强的虚假新闻检测系统的进一步探索。