Graph-of-Thoughts for Fact-Checking with Large Language Models

We present a fact-checking system developed for the 2025 Automated Verification of Textual Claims (AVeriTeC) shared task, leveraging the Graph-of-Thoughts (GoT) prompting scheme.The GoT approach facilitates iterative refinement during fact-checking by conditioning question generation on previous answers and enabling the incorporation of multiple evidence documents per question, thereby mitigating the impact of factually incorrect evidence.The efficiency requirements of the shared task are addressed by restricting the width and depth of the thought graph.Additionally, an efficient stopping criterion is derived from the dataset's Not Enough Information (NEI) label.Our system utilizes fine-tuned open-source Large Language Models (LLMs) for question generation, question answering, and final verdict prediction.Empirical results demonstrate competitive performance against top-performing systems in the AVeriTeC shared task and improvements over the baseline method.Our code is publicly available 1 .