Large Language Models for Automated Fact-Checking: A Systematic Literature Review
TL;DR
Although LLMs significantly advance fact-checking, structural challenges persist, primarily hallucinations, data scarcity, data scarcity, and a lack of temporal awareness, and a research agenda prioritizing temporal-aware architectures, Small Language Models for sustainable deployment, and human-centered evaluation metrics are proposed.
Abstract
Automated fact-checking has become critical in combating misinformation, with Large Language Models (LLMs) emerging as powerful tools for this task. This systematic literature review analyzes how LLMs have been applied to automated fact-checking, identifying effective techniques, persistent challenges, and future directions. Following the PRISMA 2020 protocol, we analyzed 85 studies published between 2018 and 2025, extracted from five academic databases (IEEE Xplore, ACM Digital Library, Scopus, Web of Science, and ScienceDirect). We found that Prompt Engineering (84.7%), Retrieval-Augmented Generation (50.6%), and Fine-tuning (55.3%) dominate the literature. While proprietary models like GPT-4 achieve the highest performance (e.g., 88.5% F1-score), they do so at a significantly higher computational cost compared to classical Transformers. Critically, only 10.6% of the studies address temporality, despite the highly dynamic nature of real-world claims. Although LLMs significantly advance fact-checking, structural challenges persist, primarily hallucinations (48.2%), data scarcity (51.8%), and a lack of temporal awareness (10.6%). To overcome these barriers, we propose a research agenda prioritizing temporal-aware architectures, Small Language Models (SLMs) for sustainable deployment, and human-centered evaluation metrics.