Abstract
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.
Abstract (translated)
快速发展的大型语言模型（LLM）技术提高了虚假新闻的风险，因为它可以自动产生越来越复杂的误导信息。以往的检测方法，包括微调的小型模型或基于LLM的检测器，往往难以应对这种动态变化的特性。在这项工作中，我们提出了一种新的框架——符号对抗学习框架（SALF），该框架通过一个代理符号学习优化过程实施了对抗性训练范式，而不是依赖数值更新。在SALF中，生成者创建误导性的叙述，而检测器则使用结构化的辩论来识别逻辑和事实上的错误进行检测；两者通过这种对抗性互动不断自我完善。 与传统的神经网络更新不同，我们用代理符号学习表示代理，其中可学习的权重由代理提示定义，并通过操作自然语言表达形式的权重、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表明，SALF表现出色：它生成了复杂且具有欺骗性的虚假新闻，在中文上降低了最先进的检测性能平均高达53.4%，在英文上则为34.2%。同时，SALF还改进了探测器的准确性，提高了对经过优化的内容进行识别的能力最多可达7.7%。 我们希望我们的工作能够激发更多关于更强大、适应性更强的虚假新闻检测系统的研究探索。
URL
https://arxiv.org/abs/2508.19633
