TELLER: A Trustworthy Framework for Explainable, Generalizable and
  Controllable Fake News Detection
Hui Liu Wenya Wang Haoru Li Haoliang Li
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}.