AI Research
Calibrated Selective Fact-Checking via Evidence Chain Evaluation
Large language models (LLMs) can achieve strong fact-checking accuracy, yet forced binary decisions conceal a critical reliability problem: systems may issue confident verdicts even when supporting evidence is weak, sparse, or internally inconsistent. We address this issue throug...
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ArXiv CS.AI
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ArXiv CS.AI
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Reliable AI fact-checking requires models to acknowledge uncertainty rather than forcing a definitive true/false. Calibrated Selective Fact-Checking allows AI to abstain from making claims when evidence is weak, improving trust and safety in AI-generated information.
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