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The paper introduces an ontology-driven framework to quantify and enforce structural consistency in document-level relation extraction datasets, reducing logical contradictions and improving model generalization when using distant supervision data.
This paper introduces a task for hallucination span detection in LLMs by aligning output tokens with input evidence, proposing an encoder-based model that uses prediction confidence to detect hallucinations without manual alignments.