Towards Transparent Fake News Detection Systems Using Large Language Models
Keywords:
Large language models, fake news detection, transparency, explainable AI, BERT, GPT, natural language processingAbstract
The rapid spread of fake news across digital platforms poses growing risks to public health, electoral integrity, and trust in institutions. Classical machine-learning and deep-learning detectors rely on handcrafted features or large labelled datasets and generalize poorly to unfamiliar topics and evolving manipulation tactics. Transformer-based large language models (LLMs) such as BERT, GPT, and RoBERTa markedly improve detection through contextual understanding and transfer learning, yet their opacity, heavy computational cost, susceptibility to bias, and vulnerability to adversarial attacks hinder trustworthy deployment. Although several surveys examine LLM-based detection, the theme of transparency remains underexplored. This paper reviews LLM-based fake news detection through the lens of transparency. Specifically, it (i) traces the evolution from classical models to transformer-based LLMs; (ii) proposes a transparency framework spanning interpretability, explanation generation, confidence calibration, bias auditing, accountability, and human oversight; (iii) annotates an end-to-end detection pipeline to indicate where each transparency mechanism can be applied; and (iv) synthesizes recent explainable and trustworthy approaches, mapping them to these dimensions. The review finds that LLMs deliver clear accuracy gains over earlier methods, but that transparency, fairness, and robustness are the decisive obstacles to real-world adoption. It concludes with a research agenda directed at auditable and socially responsible detection systems.
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