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📝 SUMMARY:
Recent studies on transformer models in Natural Language Processing (NLP) continue to explore architectural improvements, efficiency optimizations, and new applications. Key areas include modifications to the attention mechanism (e.g., sparse attention, linear attention) to handle longer sequences more efficiently, techniques for model compression and knowledge distillation, and applications beyond text, such as in computer vision and multimodal tasks. Research also focuses on understanding the internal workings and limitations of large transformer models.
🔗 SOURCES:
1. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30. (arXiv:1706.03762)
2. Tay, Y., Dehghani, M., Bahri, D., & Metzler, D. (2020). Efficient transformers: A survey. arXiv preprint arXiv:2009.06732.
3. Beltagy, I., Peters, M. E., & Cohan, A. (2020). Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150.
4. Rogers, A., Kovaleva, O., & Rumshisky, A. (2020). A primer in bertology: What we know about how bert works. Transactions of the Association for Computational Linguistics, 8, 842-866. (arXiv:2002.12327)