CREDENCE: Claim Reduction for Decomposition & Enhanced Credibility -- Semantic Metrics and Convergence Analysis
arXiv:2606.19819v1 Announce Type: new Abstract: Decomposing compound sentences into atomic, verifiable claims is a prerequisite for reliable automated fact-checking. Prior work has relied on token-overlap (Jaccard) metrics that systematically underestimate decomposition quality for paraphrastic claims, and has lacked formal termination analysis for the repair loop. We present Credence, a revised claim decomposition and evaluation framework addressing both shortcomings. Our contributions are: (1) Semantic-F1: we use BGE-large cosine similarity fidelity metric that resolves Jaccard's penalisatio
The increasing sophistication of AI models and the widespread use of generative AI necessitate more robust and reliable fact-checking and claim verification systems.
Reliable automated fact-checking is critical for combating misinformation, enhancing the trustworthiness of AI-generated content, and informing critical decision-making processes.
This research introduces a more accurate method for evaluating claim decomposition, moving beyond simple token-overlap to semantic understanding, which will improve the reliability of automated fact-checking.
- · AI Safety Researchers
- · Fact-checking organizations
- · Language Model Developers
- · Information Consumers
- · Misinformation distributors
- · Outdated fact-checking methodologies
Automated fact-checking systems become significantly more accurate and reliable, reducing false positives and negatives.
The improved reliability of fact-checking tools could lead to greater public trust in AI-powered information analysis and content moderation.
More effective misinformation detection could influence public discourse, policymaking, and even geopolitical narratives by making it harder for false claims to spread unchecked.
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Read at arXiv cs.CL