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Decomposition Dilemmas: Does Claim Decomposition Boost or Burden Fact-Checking Performance?

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arxiv 2411.02400 v2 pith:4H7PQ3CR submitted 2024-10-17 cs.IR cs.AIcs.CL

classification cs.IRcs.AIcs.CL
keywords decompositionfact-checkingperformanceanalysispipelinesclaimimpactstudies
verification ladder T0 review T1 audit T2 compute T3 formal
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Fact-checking pipelines increasingly adopt the Decompose-Then-Verify paradigm, where texts are broken down into smaller claims for individual verification and subsequently combined for a veracity decision. While decomposition is widely-adopted in such pipelines, its effects on final fact-checking performance remain underexplored. Some studies have reported improvements from decompostition, while others have observed performance declines, indicating its inconsistent impact. To date, no comprehensive analysis has been conducted to understand this variability. To address this gap, we present an in-depth analysis that explicitly examines the impact of decomposition on downstream verification performance. Through error case inspection and experiments, we introduce a categorization of decomposition errors and reveal a trade-off between accuracy gains and the noise introduced through decomposition. Our analysis provides new insights into understanding current system's instability and offers guidance for future studies toward improving claim decomposition in fact-checking pipelines.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. EMULATE: A Multi-Agent Framework for Determining the Veracity of Atomic Claims by Emulating Human Actions

    cs.CL 2025-05 conditional novelty 6.0 of 10

    EMULATE, a seven-agent LLM framework for fact-checking, reports top weighted-F1 on three benchmarks, beating FIRE by about 1 to 4 points.

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