Attention Mechanisms have become a cornerstone in modern Natural Language Processing (NLP), allowing models to focus on different parts of input sequences selectively, improving tasks such as machine translation, text summarization, and question answering. They form the backbone of Transformer models and their extensions like BERT and GPT.Attention Mechanisms continue to play a transformative role in NLP by enabling models to focus on the most relevant parts of input sequences. The PhD project ideas above cover a wide range of applications, from few-shot learning and cross-lingual NLP to explainable models and efficient real-time systems. These projects provide a strong foundation for advancing the understanding and application of attention mechanisms in cutting-edge NLP tasks.