Rotary Position Embeddings Quiz

Answers and explanations appear as you take the quiz.

1. How does RoPE inject position information into a transformer?
   • By rotating pairs of dimensions in the query and key vectors by a position-dependent angle
   • By adding a fixed vector to the input embeddings
   • By reordering the tokens randomly
   • By appending the position number as text
2. After RoPE is applied, the attention score between two tokens depends primarily on:
   • Their absolute positions only
   • Their relative distance (m minus n)
   • The vocabulary size
   • The batch size
3. What training-efficiency comparison did the YaRN paper report for context extension?
   • Exactly the same data and steps as all prior methods
   • No fine-tuning or training data of any kind
   • About 10x fewer tokens and 2.5x fewer training steps than prior methods
   • A fixed 100x increase in output tokens
4. Why do different dimension pairs rotate at different frequencies in RoPE?
   • To save memory by skipping some dimensions
   • To randomize the model's outputs
   • Because all pairs must use the same angle
   • To capture both fine local order and long-range position
5. What is the goal of techniques such as Position Interpolation and YaRN for RoPE-based models?
   • Extend the usable context length beyond the original training window using additional scaling/fine-tuning methods
   • Guarantee accurate recall at any sequence length with no evaluation
   • Remove position information from attention
   • Replace all token embeddings with absolute learned vectors
6. Does standard RoPE require learned absolute-position vectors to inject position into query and key representations?
   • Yes; it adds one learned vector for every possible sequence position
   • No; it applies position-dependent rotations to paired query/key dimensions
   • It removes query and key vectors entirely
   • It only adds position to value vectors
7. What does standard RoPE alone guarantee about a model’s context length?
   • Unlimited accurate context at every position
   • A fixed context of exactly 128 tokens
   • It does not by itself guarantee reliable use beyond the trained context length
   • Removal of the attention mechanism
8. What extended context length did the Position Interpolation paper report for its LLaMA evaluations?
   • Exactly 1,000 tokens with no position changes
   • Unlimited context with no fine-tuning
   • Up to 2 million tokens for all models
   • Up to 32,768 tokens with minimal fine-tuning