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The paper introduces evidence-aligned local composition, a method for restoring corrupted documents by inferring soft weightings over frozen domain experts from the marginal evidence of the corrupted observation, achieving high accuracy in tracking expert regions without labeled 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.