GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking
Journal:
arXiv
Published Date:
Feb 23, 2025
Abstract
Large language models (LLMs) are widely used, but they often generate subtle
factual errors, especially in long-form text. These errors are fatal in some
specialized domains such as medicine. Existing fact-checking with grounding
documents methods face two main challenges: (1) they struggle to understand
complex multihop relations in long documents, often overlooking subtle factual
errors; (2) most specialized methods rely on pairwise comparisons, requiring
multiple model calls, leading to high resource and computational costs. To
address these challenges, we propose \textbf{\textit{GraphCheck}}, a
fact-checking framework that uses extracted knowledge graphs to enhance text
representation. Graph Neural Networks further process these graphs as a soft
prompt, enabling LLMs to incorporate structured knowledge more effectively.
Enhanced with graph-based reasoning, GraphCheck captures multihop reasoning
chains which are often overlooked by existing methods, enabling precise and
efficient fact-checking in a single inference call. Experimental results on
seven benchmarks spanning both general and medical domains demonstrate a 6.1\%
overall improvement over baseline models. Notably, GraphCheck outperforms
existing specialized fact-checkers and achieves comparable performance with
state-of-the-art LLMs, such as DeepSeek-V3 and OpenAI-o1, with significantly
fewer parameters.
