TELLER：一个用于可解释、可泛化且可控的虚假新闻检测的可信框架
TELLER: A Trustworthy Framework for Explainable, Generalizable and Controllable Fake News Detection
AI总结：
针对现有虚假新闻检测方法缺乏可解释性、泛化性和可控性的问题，提出TELLER双系统框架，结合认知与决策系统生成逻辑原子和规则，在四个数据集上验证了其可行性与可信性。
摘要：
虚假新闻的泛滥已成为一个严重的社会问题，引起了工业界和学术界的广泛关注。尽管现有的基于深度学习的方法在准确检测虚假新闻方面取得了进展，但其可靠性可能因不透明的推理过程、较差的泛化能力以及与大型语言模型（LLMs）集成的固有风险而受到损害。为了解决这一挑战，我们提出了{\methodname}，一个用于可信虚假新闻检测的新框架，该框架优先考虑模型的可解释性、可泛化性和可控性。这是通过一个整合认知与决策系统的双系统框架实现的，该框架遵循上述原则。认知系统利用人类专业知识生成逻辑谓词，这些谓词指导LLMs生成人类可读的逻辑原子。同时，决策系统推导出可泛化的逻辑规则来聚合这些原子，从而能够识别输入新闻在不同领域的真实性，并增强决策过程的透明度。最后，我们在四个数据集上展示了全面的评估结果，证明了所提出框架的可行性和可信性。我们的实现可在\url{https://github.com/less-and-less-bugs/Trust_TELLER}获取。
英文摘要：
The proliferation of fake news has emerged as a severe societal problem, raising significant interest from industry and academia. While existing deep-learning based methods have made progress in detecting fake news accurately, their reliability may be compromised caused by the non-transparent reasoning processes, poor generalization abilities and inherent risks of integration with large language models (LLMs). To address this challenge, we propose {\methodname}, a novel framework for trustworthy fake news detection that prioritizes explainability, generalizability and controllability of models. This is achieved via a dual-system framework that integrates cognition and decision systems, adhering to the principles above. The cognition system harnesses human expertise to generate logical predicates, which guide LLMs in generating human-readable logic atoms. Meanwhile, the decision system deduces generalizable logic rules to aggregate these atoms, enabling the identification of the truthfulness of the input news across diverse domains and enhancing transparency in the decision-making process. Finally, we present comprehensive evaluation results on four datasets, demonstrating the feasibility and trustworthiness of our proposed framework. Our implementation is available at \url{https://github.com/less-and-less-bugs/Trust_TELLER}.
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。
