The large and ever-increasing amount of data available on the Internet coupled with the laborious task of manual claim and fact verification has sparked the interest in the development of automated claim verification systems. Several deep learning and transformer-based models have been proposed for this task over the years. With the introduction of Large Language Models (LLMs) and their superior performance in several NLP tasks, we have seen a surge of LLM-based approaches to claim verification along with the use of novel methods such as Retrieval Augmented Generation (RAG). In this survey, we present a comprehensive account of recent claim verification frameworks using LLMs. We describe the different components of the claim verification pipeline used in these frameworks in detail including common approaches to retrieval, prompting, and fine-tuning. Finally, we describe publicly available English datasets created for this task.

翻译：互联网上可用数据的规模持续增长，加之人工声明与事实核查任务繁重，推动了自动化声明验证系统的研究发展。近年来，已有多种基于深度学习和Transformer的模型被提出用于此项任务。随着大语言模型（LLMs）的引入及其在多项自然语言处理任务中的卓越表现，我们观察到基于LLM的声明验证方法激增，并涌现出检索增强生成（RAG）等新颖技术。本综述全面梳理了近期利用LLM进行声明验证的框架体系，详细阐述了这些框架中声明验证流程的各个组成部分，包括检索、提示工程与微调等常用方法。最后，本文介绍了为此任务创建的公开英文数据集。