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.
Few-shot clinical entity recognition in E nglish, F rench and S panish: masked language models outperform generative model prompting
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AutoSpecNER is a new fine-grained NER dataset for vehicle advertisements with 659 examples and 15 categories, where DeBERTa reaches 90% micro-F1 versus 43% for rules and 77.8% for the best LLM.
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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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AutoSpecNER: A Fine-Grained Named Entity Recognition Dataset for Vehicle Specification Extraction
AutoSpecNER is a new fine-grained NER dataset for vehicle advertisements with 659 examples and 15 categories, where DeBERTa reaches 90% micro-F1 versus 43% for rules and 77.8% for the best LLM.