RISE is an inference-time semantic reranking framework that refines low-confidence predictions in rhetorical role labeling using contrastively learned label representations, delivering an average +9.15 macro-F1 gain on hard examples across eight datasets and seven models.
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Introduces a new dataset and Average Severity Error metric for benchmarking LLMs on multi-label legal precedent treatment classification.
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Semantic Reranking at Inference Time for Hard Examples in Rhetorical Role Labeling
RISE is an inference-time semantic reranking framework that refines low-confidence predictions in rhetorical role labeling using contrastively learned label representations, delivering an average +9.15 macro-F1 gain on hard examples across eight datasets and seven models.
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Validate Your Authority: Benchmarking LLMs on Multi-Label Precedent Treatment Classification
Introduces a new dataset and Average Severity Error metric for benchmarking LLMs on multi-label legal precedent treatment classification.