JHTE: Jin He's Tips Engineering - A Framework for Model-Agnostic Zero-Shot Text Evaluation

This paper introduces Jin He's Tips Engineering (JHTE), a novel framework for zero-shot text evaluation using large language models. By designing precise textual instructions called ''Tips'', JHTE elicits inherent model capabilities to perform specific NLP tasks without task-specific training. We demonstrate its rational foundation through a case study on aspect-based sentiment analysis, where JHTE achieves exceptional output consistency across multiple independent runs. The framework represents a paradigm shift from traditional model engineering to capability elicitation, offering unprecedented flexibility and development efficiency across various text evaluation tasks. Its operational principle-treating LLMs as programmable computers-ensures that potential variances remain within safe bounds of granularity rather than critical misunderstandings, underscoring its reliability and practical viability.