Eliciting Zero-Shot Named Entity Recognition in Large Language Models via Instruction Semantic Elaboration

Large Language Models (LLMs) have demonstrated remarkable capabilities in Information Extraction (IE) tasks; however, their performance in zero-shot Named Entity Recognition (NER) remains suboptimal. We argue that this limitation stems not from intrinsic defects within the models, but rather from the failure of existing instruction paradigms to effectively elicit their latent capabilities. Drawing inspiration from cognitive science, we propose an instruction optimization framework termed Instruction Semantic Elaboration (ISE), which fully elicits models' latent NER capabilities by mimicking the semantic elaboration process—specifically by contrasting related concepts, decomposing constituent elements, and illustrating with concrete examples. We evaluate our framework under a zero-shot setting across four domain-specific datasets. Results demonstrate that ISE effectively and stably unlocks the intrinsic NER capabilities of LLMs, achieving a 10.99% improvement in F1 score over initial instructions and outperforming strong baselines by 6.89%. Furthermore, the framework generalizes well across model architectures and parameter scales, while remaining robust to the quality of initial instructions. This study offers a novel perspective on activating NER capabilities in LLMs, facilitating their efficient and stable deployment in real-world scenarios.