Kuiz Penyahkodan Pandang

Jawapan dan penjelasan muncul semasa anda mengambil kuiz.

1. Apakah yang membezakan penyahkodan lookahead daripada penyahkodan spekulatif standard?
   • Ia memerlukan dua model terlatih secara berasingan
   • Ia tidak menggunakan model draf yang berasingan — hanya model sasaran itu sendiri
   • Ia hanya berfungsi untuk tugas terjemahan
   • Ia mengukur model secara kekal
2. Kaedah berangka yang manakah menyokong penyahkodan pandangan ke hadapan?
   • Keturunan kecerunan
   • Transformasi Fourier
   • K-bermaksud pengelompokan
   • Lelaran Jacobi (titik tetap).
3. Apakah dua cabang selari yang berjalan pada setiap langkah?
   • Cawangan latihan dan cawangan ujian
   • Pengekod dan penyahkod
   • Cawangan pandang ke hadapan dan cawangan pengesahan
   • Model draf dan model sasaran
4. Apakah yang disimpan dalam 'kolam n-gram'?
   • Urutan berbilang token yang munasabah dilihat semasa lelaran selari
   • Sejarah sembang pengguna
   • Berat model
   • Topeng perhatian
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