부산대학교 도서관

In the world of Natural Language Processing (NLP), developing structures that not simplest carry out nicely but additionally offer apparent reasoning for their selections is paramount. Traditional gadget mastering fashions, in particular the ones based on deep gaining knowledge of, have excelled in diverse NLP duties. However, the ones models frequently act as “black containers,” providing little to no perception into their selection-making processes. This opacity can undermine the don’t forget and reliability perceived with the useful resource of customers. To deal with this trouble, we advocate a unique generative rationalization framework that endeavors to bridge the distance between high overall performance and explainability in NLP systems. This frameworks designed to concurrently make kind picks and generate unique, terrific-grained causes for the ones choices, integrating an explainable difficulty and minimum chance schooling. By providing human-readable justifications for its predictions, our framework enhances the interpretability and trustworthiness of NLP fashions. Experimental validation on two newly curated datasets demonstrates the framework’s capacity to outperform existing baselines, significantly advancing the sector of explainable AI inside NLP.