Claim Verification in the Age of Large Language Models: A Survey
This is an incremental survey that synthesizes existing research on claim verification using LLMs, aimed at researchers and practitioners in NLP and misinformation detection.
This survey addresses the challenge of automating claim verification due to the vast amount of online data, focusing on recent frameworks that use Large Language Models (LLMs) and methods like Retrieval Augmented Generation (RAG). It provides a comprehensive overview of these approaches, detailing components such as retrieval, prompting, and fine-tuning, and lists publicly available English datasets for the task.
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.