Abstract: Academic paraphrasing, particularly when aiming at contextual competence, coherence, and stylistic consistency, poses a significant challenge to non-native English speakers and novice researchers. This research seeks to create an interpretable transformer model specifically designed for paraphrasing academic texts that guarantees semantic correctness, contextual relevance, and scholarly style. Existing paraphrasing models are largely unsuitable in meeting the subtle needs of academic work, lagging in semantic preservation, fluency, scholarly style, and interpretability. In addressing these limitations, we propose T5-XAVRL (T5 with Attention Visualization and Reinforcement Learning for Style Control), an interpretable Transformer model created specifically for paraphrasing academic text. Based on the T5 architecture, T5-XAVRL adds fine-tuning for better domain adaptation, attention visualization for better transparency, and reinforcement learning to control outputs towards academic writing quality. The model is trained and tested on the ArXiv Academic Papers Dataset and demonstrates high versatility in a variety of academic environments. Developed with Python, TensorFlow, and Hugging Face Transformers, the system is made for scalability as well as performance. Experimental findings indicate that T5-XAVRL obtains a 68.7% BLEU score, greatly surpassing traditional paraphrasing models in both semantic accuracy and linguistic fluency. Far more than a paraphraser, T5-XAVRL is a trustworthy academic writing aide capable of assisting users with producing grammatically and stylistically correct scholarly work. Its interpretable outputs also increase user confidence by vividly displaying how paraphrasing choices are being made. As a whole, this study is an important step towards creating interpretable, context-sensitive, and style-sensitive paraphrasing systems for scholarly use.

A. Z. Khan, Ritu Sharma, K. Kiran Kumar, Elangovan Muniyandy, Raman Kumar, Yousef A. Baker El-Ebiary, Prema S and Osama R. Shahin, “An Interpretable Transformer-Based Approach for Context-Aware and Stylistically Aligned Academic Paraphrasing” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160660

@article{Khan2025,
title = {An Interpretable Transformer-Based Approach for Context-Aware and Stylistically Aligned Academic Paraphrasing},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160660},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160660},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {A. Z. Khan and Ritu Sharma and K. Kiran Kumar and Elangovan Muniyandy and Raman Kumar and Yousef A. Baker El-Ebiary and Prema S and Osama R. Shahin}
}