Fact in Fragments: Deconstructing Complex Claims via LLM-based Atomic Fact Extraction and Verification

2025-06-09

AI Summary

To address challenges in complex claim verification—including multi-hop reasoning difficulties, error propagation, and evidence noise—this paper proposes a dynamic iterative atomic decomposition framework. First, context-aware atomic facts are extracted using large language models; then, fine-grained, adaptive multi-hop reasoning is achieved through semantic refinement and evidence re-ranking. Crucially, the framework innovatively integrates prompt-guided chain-of-thought reasoning with iterative verification, effectively mitigating structural modeling deficiencies and intent misalignment. Evaluated on five mainstream benchmarks, the method achieves state-of-the-art accuracy while significantly enhancing interpretability and robustness to erroneous or noisy evidence.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Fact verification plays a vital role in combating misinformation by assessing the veracity of claims through evidence retrieval and reasoning. However, traditional methods struggle with complex claims requiring multi-hop reasoning over fragmented evidence, as they often rely on static decomposition strategies and surface-level semantic retrieval, which fail to capture the nuanced structure and intent of the claim. This results in accumulated reasoning errors, noisy evidence contamination, and limited adaptability to diverse claims, ultimately undermining verification accuracy in complex scenarios. To address this, we propose Atomic Fact Extraction and Verification (AFEV), a novel framework that iteratively decomposes complex claims into atomic facts, enabling fine-grained retrieval and adaptive reasoning. AFEV dynamically refines claim understanding and reduces error propagation through iterative fact extraction, reranks evidence to filter noise, and leverages context-specific demonstrations to guide the reasoning process. Extensive experiments on five benchmark datasets demonstrate that AFEV achieves state-of-the-art performance in both accuracy and interpretability.

Problem

• Traditional methods struggle with complex claims requiring multi-hop reasoning
• Static decomposition strategies fail to capture nuanced claim structure and intent
• Existing approaches suffer from reasoning errors and noisy evidence contamination

Innovation

• Iterative decomposition into atomic facts
• Dynamic evidence reranking to filter noise
• Context-specific demonstrations for reasoning