Understanding and Enhancing XLNet: A Comprehensive Exploration of Permutation Language Modeling
Dr Pankaj Malik, Vidhi Gupta, Vanshika Vyas, Rahul Baid, Parth Kala · International Journal for Research in Applied Science and Engineering Technology · 2024
Abstract: XLNet, a recent breakthrough in natural language processing, has garnered significant attention for its exceptional performance across various NLP tasks. At the core of XLNet lies Permutation Language Modeling (PLM), a novel approach that combines the strengths of autoencoding and autoregressive methods. This paper presents a comprehensive exploration of XLNet and its underlying PLM mechanism. We delve into the theoretical foundations of PLM, elucidate the XLNet architecture, and analyze its training procedure. Furthermore, we investigate strategies for enhancing XLNet's performance and efficiency, including parameter tuning, knowledge distillation, and domain adaptation. Experimental results on benchmark datasets validate the effectiveness of our proposed enhancements and provide insights into the future directions of XLNet-based research.