GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking

• URL: http://arxiv.org/abs/2502.16514v1
• Date: Sun, 23 Feb 2025 09:25:00 GMT
• Title: GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking
• Authors: Yingjian Chen, Haoran Liu, Yinhong Liu, Rui Yang, Han Yuan, Yanran Fu, Pengyuan Zhou, Qingyu Chen, James Caverlee, Irene Li,
• Abstract summary: We propose textbftextitGraphCheck, a fact-checking framework that uses extracted knowledge graphs to enhance text representation.<n>GraphCheck captures multihop reasoning chains which are often overlooked by existing methods, enabling precise and efficient fact-checking.<n> Notably, GraphCheck outperforms existing specialized fact-checkers and achieves comparable performance with state-of-the-art LLMs.
• Score: 26.6457136072675
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• GraphRunner: A Multi-Stage Framework for Efficient and Accurate Graph-Based Retrieval [3.792463570467098]
  GraphRunner is a novel graph-based retrieval framework that operates in three distinct stages: planning, verification, and execution.<n>It significantly reduces reasoning errors and detects hallucinations before execution.<n>Our evaluation using the GRBench dataset shows that GraphRunner consistently outperforms existing approaches.
  arXiv  Detail & Related papers  (2025-07-11T18:10:01Z)
• Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering [75.12322966980003]
  Large Language Models (LLMs) have shown strong inductive reasoning ability across various domains.<n>Most existing RAG pipelines rely on unstructured text, limiting interpretability and structured reasoning.<n>Recent studies have explored integrating knowledge graphs with LLMs for knowledge graph question answering.<n>We propose RAPL, a novel framework for efficient and effective graph retrieval in KGQA.
  arXiv  Detail & Related papers  (2025-06-11T12:03:52Z)
• Align-GRAG: Reasoning-Guided Dual Alignment for Graph Retrieval-Augmented Generation [75.9865035064794]
  Large language models (LLMs) have demonstrated remarkable capabilities, but still struggle with issues like hallucinations and outdated information.<n>Retrieval-augmented generation (RAG) addresses these issues by grounding LLM outputs in external knowledge with an Information Retrieval (IR) system.<n>We propose Align-GRAG, a novel reasoning-guided dual alignment framework in post-retrieval phrase.
  arXiv  Detail & Related papers  (2025-05-22T05:15:27Z)
• LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Models [54.82915844507371]
  Text-Attributed Graphs (TAGs) are ubiquitous in real-world scenarios. Despite large efforts to integrate Large Language Models (LLMs) and Graph Neural Networks (GNNs) for TAGs, existing approaches suffer from decoupled architectures. We propose PromptGFM, a versatile GFM for TAGs grounded in graph vocabulary learning.
  arXiv  Detail & Related papers  (2025-03-05T09:45:22Z)
• GraphCheck: Multi-Path Fact-Checking with Entity-Relationship Graphs [5.10832476049103]
  GraphCheck is a novel framework that converts claims into entity-relationship graphs for comprehensive verification. We introduce DP-GraphCheck, a two-stage variant that improves performance by incorporating direct prompting as an initial filtering step. Our two-stage framework generalizes well to other fact-checking pipelines, demonstrating its versatility.
  arXiv  Detail & Related papers  (2025-02-28T07:06:19Z)
• Causal Graphs Meet Thoughts: Enhancing Complex Reasoning in Graph-Augmented LLMs [4.701165676405066]
  It is critical not only to retrieve relevant information but also to provide causal reasoning and explainability.<n>This paper proposes a novel pipeline that filters large knowledge graphs to emphasize cause-effect edges.<n> Experiments on medical question-answering tasks show consistent gains, with up to a 10% absolute improvement.
  arXiv  Detail & Related papers  (2025-01-24T19:31:06Z)
• Scalable and Accurate Graph Reasoning with LLM-based Multi-Agents [27.4884498301785]
  We introduce GraphAgent-Reasoner, a fine-tuning-free framework for explicit and precise graph reasoning. Inspired by distributed graph computation theory, our framework decomposes graph problems into smaller, node-centric tasks that are distributed among multiple agents. Our framework demonstrates the capability to handle real-world graph reasoning applications such as webpage importance analysis.
  arXiv  Detail & Related papers  (2024-10-07T15:34:14Z)
• ProG: A Graph Prompt Learning Benchmark [17.229372585695092]
  Graph prompt learning emerges as a promising alternative to 'Pre-train & Fine-tune' We introduce the first comprehensive benchmark for graph prompt learning. We present 'ProG', an easy-to-use open-source library that streamlines the execution of various graph prompt models.
  arXiv  Detail & Related papers  (2024-06-08T04:17:48Z)
• Parameter-Efficient Tuning Large Language Models for Graph Representation Learning [62.26278815157628]
  We introduce Graph-aware. Efficient Fine-Tuning - GPEFT, a novel approach for efficient graph representation learning. We use a graph neural network (GNN) to encode structural information from neighboring nodes into a graph prompt. We validate our approach through comprehensive experiments conducted on 8 different text-rich graphs, observing an average improvement of 2% in hit@1 and Mean Reciprocal Rank (MRR) in link prediction evaluations.
  arXiv  Detail & Related papers  (2024-04-28T18:36:59Z)
• Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs [60.71360240206726]
  Large language models (LLMs) suffer from hallucinations, especially on knowledge-intensive tasks. Existing works propose to augment LLMs with individual text units retrieved from external knowledge corpora. We propose a framework called Graph Chain-of-thought (Graph-CoT) to augment LLMs with graphs by encouraging LLMs to reason on the graph iteratively.
  arXiv  Detail & Related papers  (2024-04-10T15:41:53Z)
• Connecting the Dots: What Graph-Based Text Representations Work Best for Text Classification Using Graph Neural Networks? [25.898812694174772]
  This work extensively investigates graph representation methods for text classification. We compare different graph construction schemes using a variety of GNN architectures and setups. Two Transformer-based large language models are also included to complement the study.
  arXiv  Detail & Related papers  (2023-05-23T23:31:24Z)
• Can Language Models Solve Graph Problems in Natural Language? [51.28850846990929]
  Large language models (LLMs) are increasingly adopted for a variety of tasks with implicit graphical structures. We propose NLGraph, a benchmark of graph-based problem solving simulating in natural language.
  arXiv  Detail & Related papers  (2023-05-17T08:29:21Z)
• FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations [114.94628499698096]
  We propose FactGraph, a method that decomposes the document and the summary into structured meaning representations (MRs) MRs describe core semantic concepts and their relations, aggregating the main content in both document and summary in a canonical form, and reducing data sparsity. Experiments on different benchmarks for evaluating factuality show that FactGraph outperforms previous approaches by up to 15%.
  arXiv  Detail & Related papers  (2022-04-13T16:45:33Z)
• Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Tracking [58.30147362745852]
  Data association across frames is at the core of Multiple Object Tracking (MOT) task. Existing methods mostly ignore the context information among tracklets and intra-frame detections. We propose a novel learnable graph matching method to address these issues.
  arXiv  Detail & Related papers  (2021-03-30T08:58:45Z)
• Model-Agnostic Graph Regularization for Few-Shot Learning [60.64531995451357]
  We present a comprehensive study on graph embedded few-shot learning. We introduce a graph regularization approach that allows a deeper understanding of the impact of incorporating graph information between labels. Our approach improves the performance of strong base learners by up to 2% on Mini-ImageNet and 6.7% on ImageNet-FS.
  arXiv  Detail & Related papers  (2021-02-14T05:28:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.