Tarisa kumberi Decoding Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Chii chinosiyanisa lookahead decoding kubva standard speculative decoding?
   • Inoda mhando mbiri dzakadzidziswa dzakasiyana
   • Iyo haishandise yakaparadzana dhizaini modhi - chete iyo yakanangwa modhi pachayo
   • Inoshanda chete pamabasa ekushandura
   • Iyo inogara ichienzanisa iyo modhi
2. Ndeipi nzira yenhamba inosimbisa kutarisa kumberi?
   • Gradient descent
   • Fourier shanduko
   • K-zvinoreva kubatanidza
   • Jacobi (fixed-point) iteration
3. Ndeapi matavi maviri anoenderana anomhanya pachinhanho chimwe nechimwe?
   • Bazi rekudzidzisa uye bazi rekuyedza
   • Encoder uye decoder
   • Bazi rekutarisa mberi uye bazi rekusimbisa
   • A dhizaini modhi uye yakanangwa modhi
4. Chii chakachengetwa mu'n-gram pool'?
   • Inonzwisisika yakawanda-token sequences inoonekwa panguva yekufananidza iterations
   • Chat nhoroondo yemushandisi
   • Zviyereso zvemuenzaniso
   • Attention masks
5. What supports correctness when lookahead decoding commits a guessed n-gram prefix?
   • Using the same model makes every unverified guess correct
   • Every candidate in the n-gram pool must be committed
   • Verification against the target's greedy predictions or an appropriate distribution-preserving sampling procedure
   • A larger window proves the generated statements are true
6. What resource tradeoff is important when deploying lookahead decoding on already busy hardware?
   • Candidate generation uses no compute or memory
   • Additional parallel work may offset the benefit of fewer sequential passes
   • Every larger window must lower wall-clock latency
   • The target weights are automatically compressed to zero size
7. Why should a lookahead candidate remain provisional until target verification?
   • Parallel iteration guesses can differ from the target's valid continuation
   • Every cached n-gram has already been proven correct by storage alone
   • The lookahead branch permanently changes the target weights
   • Verification is needed only to determine the output filename
8. Does a standard training-free lookahead iteration update the target model's learned weights?
   • Yes; each candidate applies a gradient update to every parameter
   • Yes; verified n-grams become a new training dataset immediately
   • Only the tokenizer's vocabulary is retrained at every step
   • No; it changes candidate token states and verifies outputs while keeping the model weights fixed