questions.md

Based on the content of "Attention Is All You Need," PDF file here are 10 questions that Vision RAG PoC with ColPali could potentially answer.

1. What is the Transformer model, and how does it differ from recurrent and convolutional neural networks?

2. What are the main advantages of self-attention mechanisms over recurrent models?

3. How does multi-head attention work, and why is it beneficial in the Transformer architecture?

4. What role does positional encoding play in the Transformer model, and how is it implemented?

5. What are the key components of the Transformer’s encoder and decoder stacks?

6. How is the Transformer optimized for faster training, and what are its training requirements?

7. What are the main applications of scaled dot-product attention in the Transformer?

8. What were the BLEU scores achieved by the Transformer on the WMT 2014 English-to-German and English-to-French translation tasks?

9. How does the Transformer handle long-range dependencies more effectively than previous models?

10. What regularization techniques are used in the Transformer, and how do they improve its performance?