ProFiNER: Progressive Entity Calibration and Conflict-Aware Inference for Zero-Shot Named Entity Recognition

Zero-shot named entity recognition without task-specific annotations provides a flexible solution for adapting entity extraction to new domains and label spaces. However, direct prompting with large language models often suffers from incomplete entity discovery, inaccurate boundaries, type confusion, and inconsistent predictions among competing candidates. To address these distinct failure modes systematically, we propose ProFiNER, a progressive inference framework for robust zero-shot NER. Specifically, ProFiNER first integrates type-agnostic mention mining with decomposed type-wise question answering and iterative sampling to build a high-recall candidate entity pool. It then selectively refines uncertain entity boundaries and calibrates candidate types through label-wise score normalization and pairwise comparison in an extended label space containing an explicit non-entity category. Finally, structurally incompatible candidates are represented by a conflict graph and jointly resolved through evidence-based adjudication and global selection. Experiments on multiple NER benchmarks with different language model backbones demonstrate that ProFiNER consistently improves entity recognition over direct prompting. Further ablation analyses demonstrate that our type calibration mechanism effectively alleviates semantic confusion, while conflict-aware global inference significantly enhances the structural consistency of the final predictions.