Generative Large Language Models in Automated Fact-Checking: A Survey

Abstract

The rapid spread of false and misleading information on online platforms poses a growing societal challenge, overwhelming the capacity of manual fact-checking and increasing the demand for scalable, reliable automation. Recent advances in generative large language models (LLMs) have broadened the scope of automated fact-checking beyond accuracy-driven prediction. LLMs are now integral components of fact-checking pipelines, supporting tasks such as generating new data, performing and assisting with fact verification, and shaping how fact-checking systems are evaluated. This survey provides a comprehensive overview of the role of generative LLMs in automated fact-checking, based on a systematic review of 199 research papers. We introduce a unifying taxonomy that captures how generative LLMs are integrated into fact-checking workflows and analyze their use across core fact-checking tasks, dataset construction and augmentation strategies, task formulations, and evaluation practices. Additionally, we investigate the impact of generative LLMs in multilingual and low-resource settings in fact-checking, highlighting trends, limitations, and gaps in current research. By consolidating fragmented research efforts and identifying methodological patterns, limitations, and open challenges, this survey maps the current state of generative LLMs in automated fact-checking. It aims to support researchers in developing more reliable, interpretable, and inclusive fact-checking systems, while outlining promising directions for future research in this rapidly evolving field.