SAM-NER: Semantic Archetype Mediation for Zero-Shot Named Entity Recognition
SAM-NER addresses a critical brittleness in zero-shot NER systems: LLMs struggle when entity schemas shift across domains because their internal semantic organization misaligns with novel label definitions. The proposed framework uses an intermediate archetype space to stabilize transfer, decoupling entity discovery from direct label mapping. This tackles a real production pain point for practitioners deploying NER at scale across heterogeneous domains, where fine-tuning is infeasible and schema drift causes systematic failures. The three-stage approach (entity discovery, abstract mediation, label projection) represents a meaningful methodological advance for practitioners working with LLMs on structured extraction tasks.
Modelwire context
ExplainerSAM-NER's key insight is that zero-shot NER fails not because LLMs can't find entities, but because their learned semantic space doesn't align with novel label definitions across domains. The archetype layer acts as a translation mechanism, decoupling discovery from naming.
This connects directly to the schema refinement problem covered in EGREFINE and SC-Taxo from early May. Those papers tackled schema ambiguity in text-to-SQL and taxonomy generation respectively. SAM-NER extends the same principle to entity labeling: when schemas shift, the model's internal representations become misaligned with the new task. The three-stage mediation approach mirrors the constraint-guided execution pattern in RunAgent, trading some flexibility for determinism and reliability across domain boundaries.
If SAM-NER's archetype space generalizes to unseen domains not in the training set (a true zero-shot test), that validates the core claim. Watch whether follow-up work applies this to other structured extraction tasks (relation extraction, event detection) within the next six months. If the method only works well on domains similar to training data, the archetype layer is domain-specific adaptation in disguise.
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