The Evolution of Current Fact-Checking Pipeline with Influence of Large Language Models and Transformers: A Review

The rapid development of generative artificial intelligence tools has contributed to the spread of misinformation and falsehoods, making it easy to fabricate lies, misinformation, and rumors across various sensitive fields such as health and politics. However, these tools are a double-edged sword; they can also be used to detect and limit the spread of misinformation and have become essential in the process of fact-checking. Therefore, in this paper, we review the evolution of the automated fact-checking pipeline in the context of these emerging technologies. Based on a structured literature search, we review and synthesize 81 studies across four major stages: claim detection, evidence collection, veracity prediction, and explanation generation. Moreover, we explicitly distinguish claim-level verification from fake news detection and propose two taxonomies: one organizing veracity prediction models by architectural family, from traditional machine learning to large language models, and one classifying fact-checking and fake-news datasets into four classes: general, multimodal, multilingual, and topic specific. This classification provides an extensive comparative analysis of their attributes, annotation approaches, and open-access sources, facilitating future benchmarking. Finally, we explore open pipeline challenges—including LLM hallucinations and operational constraints—and outline future research directions.