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аналитика - Zero-Shot Named Entity Recognition
Self-Improving Framework for Zero-Shot Named Entity Recognition with Large Language Models
Proposing a training-free self-improving framework for zero-shot NER with LLMs significantly improves performance.
Enriching Prompts with Definitions and Guidelines for Improved Zero-Shot Named Entity Recognition
Providing definition and guidelines in the prompt can improve the performance and robustness of instruction-tuned language models for zero-shot named entity recognition, especially on unseen entity types.
Zero-Shot Named Entity Recognition on Italian Language
SLIMER-IT, an instruction-tuning approach for zero-shot named entity recognition, leverages prompts enriched with definitions and guidelines to outperform state-of-the-art models on unseen entity types in Italian.
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