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Kuis Decoding Paralel Kerangka Pemikiran

Uji kerangka Kerangka Pemikiran, perluasan titik independen, penguraian kode batch, overhead, autoregresi per titik, dan pengumpulan jawaban akhir.

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Apa yang dihasilkan oleh tahap pertama dari Skeleton-of-Thought?

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  1. Apa yang dihasilkan oleh tahap pertama dari Skeleton-of-Thought?
    • Jawaban akhir yang lengkap
    • Pos pemeriksaan model yang disempurnakan
    • Kerangka singkat judul poin
    • Satu set label pelatihan
  2. Mengapa tahap perluasan titik dapat berjalan secara paralel?
    • Karena setiap poin bergantung pada poin sebelumnya
    • Karena poin-poin tersebut sebagian besar tidak tergantung satu sama lain
    • Karena model tersebut dilatih ulang untuk paralelisme
    • Karena token dihasilkan secara terbalik
  3. Apa target hambatan utama SOT dalam decoding LLM normal?
    • Latensi token demi token yang berurutan
    • Kecepatan membaca disk
    • Ukuran kosakata Tokenizer
    • Bandwidth jaringan ke pengguna
  4. Which answer structure is a good fit for Skeleton-of-Thought's parallel expansion?
    • A proof whose next step must use the previous step's newly derived result
    • A single fixed one-word output with no expansion
    • A procedure that requires each section to wait for all earlier outputs
    • Several sections that can be developed largely independently
  5. What extra work should a Skeleton-of-Thought latency comparison include?
    • Only the longest point while ignoring every other stage
    • Generating the outline, coordinating point expansion, and assembling the final response
    • Mandatory full model retraining for every answer
    • A guarantee that overhead is always exactly the same fixed duration
  6. How can local batched Skeleton-of-Thought decoding expand multiple points?
    • Generate every token of every paragraph in one complete forward pass
    • Retrain a new model for each heading
    • Advance the point sequences together across repeated decoding steps
    • Remove autoregressive dependencies within every point automatically
  7. If Skeleton-of-Thought reduces answer latency, what other assessment remains necessary before adopting the workflow?
    • Treat the speedup as proof that all factual claims are correct
    • Measure only the spelling of the model's name
    • Assume outline ordering guarantees that every section agrees
    • Check whether the assembled response remains coherent and meets answer-quality requirements
  8. After concurrent point expansion, how should Skeleton-of-Thought outputs be assembled into the answer?
    • Associate expansions with their skeleton points and combine them in the intended outline order
    • Shuffle the sections randomly regardless of their headings
    • Treat the first finished point as the entire answer
    • Discard the outline and keep only token counts