Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Model

This August 2025 paper explores the critical area of fact-checking and factuality evaluation in Large Language Models (LLMs). It systematically analyzes the challenges of misinformation generation, particularly hallucinations, which are factually incorrect but fluent outputs from LLMs. The paper investigates various mitigation strategies, including fine-tuning, instruction tuning, and Retrieval-Augmented Generation (RAG), which grounds LLM outputs in external knowledge. It further examines evaluation metrics, datasets, and prompting strategies used to assess and enhance the factual accuracy of these models, highlighting the need for more robust, explainable, and domain-specific fact-checking frameworks. The review concludes by identifying open issues and future research agendas to foster more trustworthy and context-aware LLMs. Source: https://arxiv.org/pdf/2508.03860