DiZiNER improves zero-shot NER by having multiple LLMs annotate texts and using a supervisor to refine instructions from their disagreements, reaching SOTA on 14 of 18 benchmarks with +8 F1 gains.
arXiv preprint arXiv:2402.12801
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cs.CL 2years
2026 2verdicts
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A pipeline generates synthetic Dutch medical dialogues via fine-tuned LLM and evaluates them quantitatively and qualitatively, showing feasibility but gaps in naturalness.
citing papers explorer
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DiZiNER: Disagreement-guided Instruction Refinement via Pilot Annotation Simulation for Zero-shot Named Entity Recognition
DiZiNER improves zero-shot NER by having multiple LLMs annotate texts and using a supervisor to refine instructions from their disagreements, reaching SOTA on 14 of 18 benchmarks with +8 F1 gains.
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Generating High Quality Synthetic Data for Dutch Medical Conversations
A pipeline generates synthetic Dutch medical dialogues via fine-tuned LLM and evaluates them quantitatively and qualitatively, showing feasibility but gaps in naturalness.