Enterprise AI Analysis: Natural Language Processing
DagFC: Dependency-Aware Fact-Checking via Claim-Constructed Knowledge Graphs and Large Language Models
Abstract: Fact-checking, also referred to as fact verification, is essential for evaluating the accuracy of claims and curbing the dissemination and influence of misinformation. Recent advancements in Large Language Models (LLMs) have enabled their use in automated fact-checking systems. Existing approaches often neglect the dependency between sub-claims and verify them in isolation. To address this, we propose DagFC, a novel LLM-based framework that performs Dependency-Aware Task Generation, Scheduling and Processing for Fact-Checking. DagFC constructs Knowledge Graphs (KGs) from claims to guide the decomposition of fact-checking problems and build dependent verification sub-tasks that capture the interrelations between sub-claims. This dependency-aware approach ensures more coherent and accurate verification by integrating intermediate results. Extensive experiments demonstrate that DagFC outperforms state-of-the-art methods in both accuracy and Macro-F1 score, offering practical value for research and public use.
Authors: Zhouhui Wu, Zhuohua Yang, Jiaojiao Jiang, Shuiqiao Yang, Nan Sun
Executive Impact & Key Metrics
DagFC represents a significant leap forward in automated fact-checking, delivering robust, dependency-aware verification that improves accuracy and efficiency in information systems and NLP applications.
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Macro-F1 Improvement (HoVer)
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Accuracy Improvement (FEVEROUS)
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Total Citations
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Total Downloads
Deep Analysis & Enterprise Applications
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Framework Overview
DagFC is an LLM-based framework designed for Dependency-Aware Task Generation, Scheduling, and Processing for Fact-Checking. It transforms complex claims into a flexible workflow of simple, dependent verification tasks. The framework utilizes two types of tasks: clarification tasks to resolve ambiguous entities and retrieval tasks to collect evidence. After all tasks, an LLM verifies the claim using the collected evidence.
Knowledge Graph & Task Scheduling
A core innovation of DagFC is the dynamic construction of a Knowledge Graph (KG) from each claim. This KG serves as a structural backbone for generating dependent verification tasks, capturing entities, relations, and properties. Ambiguous entities are resolved via a scheduled clarification process, where tasks are ordered by 'difficulty' to ensure easier-to-resolve entities are handled first, facilitating downstream tasks.
Benchmarking Performance
Experiments on FEVEROUS and HoVer datasets demonstrate DagFC's superior performance, particularly on multi-hop claims where dependency awareness is crucial. It consistently outperforms state-of-the-art LLM-based baselines in both accuracy and Macro-F1 score, validating the effectiveness of its dependency-aware framework design.
Identified Limitations & Future Work
The analysis reveals common errors in KG construction (e.g., information loss), evidence retrieval failures, document summarization errors (e.g., hallucinations), and claim verification issues. Future work suggests iterative KG refinement, query generation refinement, and exploring alternative verification methods like contrastive arguments to enhance robustness.
DagFC: Dependency-Aware Fact-Checking Workflow
Step 1: KG Construction
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Step 2: Ambiguous Entities Resolving
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Step 3: Claim Verification
DagFC introduces a novel LLM-based framework for fact-checking. It constructs Knowledge Graphs (KGs) from claims to guide the decomposition of fact-checking problems and build dependent verification sub-tasks.
10.48%
Macro-F1 Improvement on HoVer (2-hop) over Direct Prompt
DagFC consistently outperforms state-of-the-art LLM-based baselines across complex multi-hop reasoning datasets. By integrating dependency-aware task generation and KG construction, it achieves substantial gains in fact verification accuracy and Macro-F1 score.
| Strategy | Benefits | Drawbacks |
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| KG-driven |
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| Decomposition-based |
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| DagFC (Hybrid) |
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The Power of Dependency Awareness
Challenge: Existing LLM-based approaches often neglect the dependency between sub-claims and verify them in isolation. For complex claims, particularly those requiring multi-hop reasoning, the interconnections between sub-claims are crucial, as verifying each one independently often fails to capture the full context and reasoning needed for accurate verification.
DagFC's Solution: DagFC introduces a novel task generation strategy that dynamically constructs a Knowledge Graph (KG) from each input claim. The KG serves as a backbone for generating dependent verification tasks, leveraging its structural properties to connect entities and integrate knowledge effectively. This helps with building dependent verification tasks focusing on one atomic information piece, thereby reducing the difficulties of processing each verification task and improving the overall accuracy of the fact-checking process.
Result: The Full Dependency framework, as implemented in DagFC, achieves the best performance when verifying higher-hop claims (e.g., 3-hop and 4-hop claims), significantly outperforming partial or no dependency approaches. This validates the importance of its dependency-aware architecture.
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Your AI Implementation Roadmap
A structured approach to integrating advanced AI, from initial assessment to ongoing optimization.
Phase 1: Strategic Assessment & Planning
Comprehensive analysis of current fact-checking workflows, identifying integration points for DagFC, defining KPIs, and outlining a tailored deployment strategy. Includes data preparation for Knowledge Graph construction.
Phase 2: Pilot Deployment & Customization
Initial implementation of DagFC on a subset of claims or a specific department, involving fine-tuning LLM prompts, adapting KG schema, and customizing clarification/retrieval task logic to enterprise-specific evidence sources.
Phase 3: Full-Scale Integration & Training
Rollout across the organization, integration with existing information systems, and training for users on interpreting DagFC's dependency-aware verification results and utilizing its interactive prototype.
Phase 4: Performance Monitoring & Optimization
Continuous monitoring of DagFC's fact-checking accuracy, efficiency, and error rates. Iterative improvements based on feedback, new research (like error analysis insights), and evolving enterprise needs to maximize ROI.
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