Maswali yanayounganishwa na mwongozo · Ngumu Kiwango

Maswali ya Kukisia ya Uthibitishaji wa Sampuli

Hufanyia majaribio sampuli za kubahatisha, uwezekano wa kukubalika na kusahihisha, rasimu ya gharama na vikwazo kwenye madai yake ya utendakazi.

Njia za mwongozo zinazohusianaUthibitishaji wa Sampuli za Kukisia
Swali 1 ya 8

Ni nini wazo kuu nyuma ya sampuli za kubahatisha?

Hakiki kila swali8 maswali

Majibu na maelezo huonekana unapojibu maswali.

  1. Ni nini wazo kuu nyuma ya sampuli za kubahatisha?
    • Tumia muundo mdogo wa rasimu ili kupendekeza tokeni nyingi ambazo muundo mkubwa huthibitisha sambamba
    • Funza modeli moja kubwa kuruka tabaka kwa nguvu
    • Hifadhi kila sentensi inayowezekana mapema
    • Badilisha sampuli na usimbuaji wa pupa ili kuokoa muda
  2. Kwa nini sampuli za kubahatisha hazidunishi ubora wa matokeo?
    • Kwa sababu mfano wa rasimu ni sahihi kila wakati
    • Kwa sababu sheria ya uthibitishaji hufanya usambazaji wa mwisho kufanana na sampuli halisi lengwa
    • Kwa sababu inafanya kazi kwa vidokezo vifupi tu
    • Kwa sababu ni wastani wa matokeo ya aina mbili
  3. In exact speculative sampling, what happens when a drafted token is rejected?
    • It outputs the rejected draft token unchanged
    • It discards the entire conversation and restarts
    • The algorithm samples from a corrected residual distribution at that position
    • It permanently changes the target model
  4. Under exact speculative sampling, what is guaranteed about the output distribution?
    • It is always deterministic
    • It matches the draft model’s distribution instead
    • It is more diverse than every target-model output
    • It matches the target model’s sampling distribution
  5. What information does the target verification pass provide in exact speculative sampling?
    • Target probabilities for proposed positions, used for acceptance tests and residual resampling
    • A guarantee that no target-model computation is needed
    • Only the draft model’s next-token distribution
    • A corrected token sequence without any sampling
  6. Which description correctly distinguishes two self-speculative approaches?
    • Both use only an unrelated small draft model
    • Medusa adds prediction heads, while EAGLE uses feature-space proposals
    • Medusa is a tokenizer and EAGLE is a reward model
    • Both remove the target model from decoding
  7. The draft assigns probability 0.8 to a token and the target assigns 0.4. What is the speculative acceptance probability?
    • 0.2
    • 0.4
    • 0.5
    • 1.0
  8. What most limits speculative sampling’s realized speedup?
    • The color of the model’s interface
    • The target model’s name length
    • Whether the prompt contains punctuation
    • The draft-model cost and how much proposed work the target accepts