JHTE for ABSA

This paper presents a full empirical implementation and demonstration of the JHTE (Jin He’s Tips Engineering) framework on the task of Aspect-Based Sentiment Analysis (ABSA). The core claim of JHTE is that large language models already encapsulate rich linguistic knowledge; instead of training or fine-tuning, one only needs to elicit their inherent capabilities through carefully designed natural language instructions (Tips). Taking ABSA as a case study, we systematically evaluate JHTE on the SemEval-2014 Laptop14 dataset. The experiments consist of two stages: first, using a baseline prompt, we achieve an accuracy of 79.78%; second, by introducing the rule that “absence of explicit opinion words implies neutral”, we raise the accuracy to 84.64% and the macro-average F1 to 81.95%. Under zero training, zero fine-tuning, and zero API cost, this result surpasses the BERT baseline and several state-of-the-art supervised models (e.g., DAGF). The paper fully documents the key steps of prompt iteration, model invocation, and result parsing, demonstrating the reproducibility and practicality of the “capability elicitation” paradigm of JHTE. The study shows that JHTE not only significantly outperforms traditional methods in development efficiency and flexibility, but also achieves comparable or even superior performance to leading supervised models, marking a paradigm shift from “model engineering” to “cognitive guidance”. This work provides a new path for NLP research under resource-constrained conditions and lays a foundation for applying the JHTE framework to a wider range of language tasks.