Published: Aug. 14, 2024

In academic writing, citations play an essential role in ensuring the attribution of ideas, supporting scholarly claims, and enabling traceability knowledge across disciplines. However, manual process citation generation is often time-consuming prone to errors, leading inconsistencies that can undermine credibility work. The novel approach explored this study leverages advanced machine learning techniques automate process, offering a significant improvement both accuracy efficiency. Through integration contextual semantic features, model demonstrates superior ability replicate complex patterns, adapt various disciplines, generate contextually appropriate with high precision. results rigorous experiments reveal not only outperforms traditional tools but also exhibits robust scalability, making it well-suited for large-scale applications. This research contributes field automated providing powerful tool enhances quality integrity communication.

Language: Английский