AI RESEARCH
Semantic Reranking at Inference Time for Hard Examples in Rhetorical Role Labeling
arXiv CS.CL
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ArXi:2605.18007v1 Announce Type: new Rhetorical Role Labeling (RRL) assigns a functional role to each sentence in a document and is widely used in legal, medical, and scientific domains. While language models (LMs) achieve strong average performance, they remain unreliable on hard examples, where prediction confidence is low. Existing approaches typically handle uncertainty implicitly and treat labels as discrete identifiers, overlooking the semantic information encoded in label names. We