Explain Transformer Positional Encoding
Company: Cadence
Role: Machine Learning Engineer
Category: Machine Learning
Difficulty: medium
Interview Round: Technical Screen
In a Transformer-based sequence model, explain why positional encoding is needed. Describe how positional information is incorporated into token representations, how sinusoidal positional encoding is computed, and how it compares with learned positional embeddings. Also discuss why this matters for large language models and what can go wrong if positional information is missing.
Quick Answer: This question evaluates understanding of positional encoding in Transformer architectures, including how positional information is integrated into token representations, the distinction between sinusoidal and learned positional embeddings, and the implications for large language models.