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🗞️   Advancements in Natural Language Processing: Exploring Transformer-Based Architectures for Text Understanding

science

:2503.20227v1 Announce Type: cross Abstract: Natural Language Processing (NLP) has witnessed a transformative leap with the advent of transformer-based architectures, which have significantly enhanced the ability of machines to understand and generate human-like text. This paper explores the advancements in transformer models, such as BERT and GPT, focusing on their superior performance in text understanding tasks compared to traditional methods like recurrent neural networks (RNNs). By analyzing statistical properties through visual representations-including probability density functions of text length distributions and feature space classifications-the study highlights the models' proficiency in handling long-range dependencies, adapting to conditional shifts, and extracting features for classification, even with overlapping classes.

arXiv     Mar 27, 2025