Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

Fi agbara mu Olukọ ni adanwo Awọn awoṣe Ọkọọkan

Checks how ground-truth prefixes train a sequence decoder, how causal masks support parallel training, and why inference differs.

Jẹmọ itọsọna awọn ọnaMuwon Olukọni Ni Awọn awoṣe Ọkọọkan
Ibeere 1 ti 8

Lakoko ipaniyan olukọ, kini ifunni bi titẹ sii ni igbesẹ iyipada kọọkan?

Awotẹlẹ gbogbo ibeere8 ibeere

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

  1. Lakoko ipaniyan olukọ, kini ifunni bi titẹ sii ni igbesẹ iyipada kọọkan?
    • Ilẹ-otitọ ti tẹlẹ àmi
    • Awọn awoṣe ile ti ara ti tẹlẹ asọtẹlẹ
    • A ID àmi
    • Ik o wu àmi
  2. During teacher-forced training, what target is paired with decoder input y_(t−1)?
    • The previous ground-truth token
    • The next target token y_t
    • A token sampled from the validation set
    • The maximum-probability output from an earlier training run
  3. Aiṣedeede idanwo-irin-irin ti o ṣẹlẹ nipasẹ ipaniyan olukọ ni a mọ si:
    • Awọn gradients ti nparun
    • Ipo iṣubu
    • Iyasọtọ ifihan
    • Aami jijo
  4. Ninu oluyipada Ayipada, ẹrọ wo ni o jẹ ki ikẹkọ fi agbara mu olukọ ṣiṣẹ ni afiwera kọja awọn ipo?
    • Jade kuro
    • Ipese deede
    • Iwadi ina
    • Iboju ifojusọna idi kan (wo iwaju).
  5. Ni akoko itọkasi, kilode ti a ko le lo ipaniyan olukọ?
    • Ko si awọn ami-ilẹ-otitọ ọjọ iwaju lati jẹun
    • Iṣẹ pipadanu ko si
    • GPU ti lọra pupọ
    • Awọn iboju iparada ti wa ni alaabo
  6. Ipadanu wo ni a maa n lo ni igbesẹ kọọkan lakoko ikẹkọ fi agbara mu olukọ?
    • Itumọ onigun mẹrin aṣiṣe lori aise logits
    • Cross-entropy laarin awọn ti anro pinpin ati otito tókàn àmi
    • Mitari pipadanu lori awọn ifibọ
    • Àdánù ibajọra Cosine
  7. The target sequence is “red, blue, green.” Under teacher forcing, which token is supplied when training the prediction of “green”?
    • The model’s prediction for “green”
    • The future token after “green”
    • The ground-truth previous token “blue”
    • A randomly sampled vocabulary item
  8. At decoder position t, which tokens must a causal attention mask block?
    • The source sequence
    • All earlier target tokens
    • The current position’s own input
    • Target tokens at positions later than t