LLM as Auto-Prompt Engineer: Automated NER Prompt Optimisation

The emergence of Large Language Models (LLMs) has revolutionised natural language processing capabilities. However, despite these advances, effectively optimising prompts for knowledge extraction tasks like Named Entity Recognition (NER) remains challenging. This paper presents a zero-shot automated prompt engineering approach that decomposes the NER task into two phases: entity boundary detection and entity classification. Our method incorporates structured task analysis, automated prompt generation, test case generation, and iterative optimisation, requiring no labelled training examples. This decomposition allows for more precise entity recognition while maintaining the efficiency. Through experimentation on the CoNLL-2003 dataset using standard exact-match evaluation metrics, our approach demonstrates improvements over unified methods, achieving a 75.39% F1 score compared to baseline approaches (72.90%). The key contributions include: (1) A structured pipeline for zero-shot automated prompt engineering in NER tasks that addresses the challenges of prompt design and optimisation; (2) A two-phase approach to NER tasks that separates boundary detection from entity classification; and (3) Experimental results demonstrating the effectiveness of our approach compared to existing zero-shot approaches in NER tasks.