quiz bu lëkkale ak tegtal · Diggante Niveau
Quiz ngir bàyyi xel ci palanteer buy gliise
Xoolaat masku bàyyi xel ci dëkk bi ak eskaalu palanteer bu takku, ak ay barab yuy jot, ay cache yuy wër, Gemma 2 diisaay, ak ay token global Longformer.
Laaj 1 de 8
With fixed window W, how does the pairwise attention term N times W scale as sequence length N increases?
Xoolaatal laaj bu nekk8 laaj
Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.
- With fixed window W, how does the pairwise attention term N times W scale as sequence length N increases?
- Quadratically in N despite W being fixed
- Exponentially in the vocabulary size by definition
- It never changes as any tokens are added
- Linearly in N
- How can stacked local attention layers propagate information beyond one layer’s direct neighborhood?
- By guaranteeing perfect recall of every earlier token
- Through intermediate representations passed across successive layers
- By randomly moving the original input’s coordinates
- By eliminating the need for all learned weights
- For a purely local causal attention layer, why can cache policy discard keys older than its active window?
- Those keys are outside that layer’s allowed direct attention links
- Every model layer must therefore have an identical whole-model memory bound
- Older tokens are always irrelevant to every possible task
- The model’s original weights have been deleted
- What attention pattern does the Gemma 2 report describe across its layers?
- Only one permanently disconnected input token
- No positional or attention computation anywhere
- Alternating local sliding-window and global attention
- A mandatory single identical local neighborhood for all models
- Ngir palanteer buy gliise bu tollu ci W, ban caabi lañu mëna laajte ci barab bi ma topp?
- Position yi ci kanam yépp ginaaw i
- Position i dindi W yokk 1 ba i
- Position 0 kese
- ab ensemble bu bari ci position yépp
- In the Mistral report’s rolling cache, which slot stores the keys and values for timestep i with buffer size W?
- A new permanent slot numbered i+W with no reuse
- Only slot zero for every timestep
- The slot at i modulo W
- A slot chosen by the output token’s alphabetical order
- Which context_len value is listed in the original Mistral 7B architecture table, separate from its larger theoretical stacked attention span?
- 4096 tokens
- 131072 tokens
- 32 tokens
- 8192 tokens
- In Longformer’s stated symmetric global-attention design, how does a selected global token connect to other sequence tokens?
- It attends to them and they can attend to it
- It can receive no attention from any other token
- It must ignore every token outside its local window
- It requires all non-global tokens to be deleted