Hongyi Cen

Researchers introduce AtomEval, a new evaluation framework that addresses a critical gap in how fact-checking systems are tested against adversarial attacks. Current metrics often fail to detect when adversarial rewrites corrupt the semantic meaning of claims, instead treating surface-level similarity as success. AtomEval decomposes claims into atomic components (subject-relation-object-modifier) and uses Atomic Validity Scoring to catch factual corruption, revealing that stronger language models do not necessarily generate more effective adversarial claims when evaluated rigorously.