Symbolic Augmentation Closes a Canonical-Equivalence Blind Spot in Neural Fact-Checkers
英文标题：Symbolic Augmentation Closes a Canonical-Equivalence Blind Spot in Neural Fact-Checkers
arXiv:2607.16212v1 Announce Type: new Abstract: Large language models hallucinate numbers and units when summarizing scientific text, a failure mode that can silently invert a scientific claim. We recast the detection of such errors as typed verification: we introduce a five-class typed-quantity er
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arXiv:2607.16212v1 Announce Type: new Abstract: Large language models hallucinate numbers and units when summarizing scientific text, a failure mode that can silently invert a scientific claim. We recast the detection of such errors as typed verification: we introduce a five-class typed-quantity er
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