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        <title>Graph-of-Thoughts for Fact-Checking with Large Language Models</title>
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    <abstract>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 conditioningquestion generation on previous answers and enabling the incorporation of multiple evidence documents per question, thereby mitigatingthe impact of factually incorrect evidence. The efficiency requirements of the shared task are addressed by restricting the width and depthof 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.</abstract>
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%0 Conference Proceedings
%T Graph-of-Thoughts for Fact-Checking with Large Language Models
%A Rolinger, Sascha
%A Liu, Jin
%Y Akhtar, Mubashara
%Y Aly, Rami
%Y Christodoulopoulos, Christos
%Y Cocarascu, Oana
%Y Guo, Zhijiang
%Y Mittal, Arpit
%Y Schlichtkrull, Michael
%Y Thorne, James
%Y Vlachos, Andreas
%S Proceedings of the Eighth Fact Extraction and VERification Workshop (FEVER)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 978-1-959429-53-1
%F rolinger-liu-2025-graph
%X 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 conditioningquestion generation on previous answers and enabling the incorporation of multiple evidence documents per question, thereby mitigatingthe impact of factually incorrect evidence. The efficiency requirements of the shared task are addressed by restricting the width and depthof 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.
%R 10.18653/v1/2025.fever-1.21
%U https://aclanthology.org/2025.fever-1.21/
%U https://doi.org/10.18653/v1/2025.fever-1.21
%P 266-273

Markdown (Informal)

[Graph-of-Thoughts for Fact-Checking with Large Language Models](https://aclanthology.org/2025.fever-1.21/) (Rolinger & Liu, FEVER 2025)

• Graph-of-Thoughts for Fact-Checking with Large Language Models (Rolinger & Liu, FEVER 2025)

ACL

• Sascha Rolinger and Jin Liu. 2025. Graph-of-Thoughts for Fact-Checking with Large Language Models. In Proceedings of the Eighth Fact Extraction and VERification Workshop (FEVER), pages 266–273, Vienna, Austria. Association for Computational Linguistics.