Idanwo ti o sopọ mọ itọsọna · Lile Ipele

Idanwo Awoṣe Permutation XLNet

Checks permutation factorization, two-stream attention, Transformer-XL recurrence, and how benchmark claims depend on comparable settings.

Jẹmọ itọsọna awọn ọnaXlnet Permutation Modeli
Ibeere 1 ti 8

Ète ìdánilẹ́kọ̀ọ́ wo ni XLNet ń lò?

Awotẹlẹ gbogbo ibeere8 ibeere

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

  1. Ète ìdánilẹ́kọ̀ọ́ wo ni XLNet ń lò?
    • Awoṣe ede Permutation
    • Awoṣe ede bojuboju
    • Rọpo àmi erin
    • Next gbolohun asọtẹlẹ
  2. Ailagbara BERT wo ni XLNet ṣe apẹrẹ pataki lati koju?
    • BERT kere ju
    • Àmi [MASK] fa aiṣedeede pretrain/tun-tune ati pe o gba ominira-ami-ami-iṣiro.
    • BERT ko le mu awọn gbolohun ọrọ kukuru mu
    • BERT ko ni awọn koodu aiyipada ipo
  3. Kini akiyesi ara ẹni ṣiṣan-meji XLNet ya sọtọ?
    • Encoder ati decoder fẹlẹfẹlẹ
    • Ikẹkọ ati awọn aworan atọka
    • Ṣiṣan akoonu ati ṣiṣan ibeere kan
    • Àmi ati awọn ifibọ gbolohun
  4. Ṣe XLNet dapọ awọn ọrọ inu gbolohun ọrọ nipa ti ara bi?
    • Bẹẹni, laileto o tun paṣẹ awọn ami-ami gangan
    • Bẹẹni, sugbon nikan nigba itanran-yiyi
    • Rara, kii ṣe iyipada aṣẹ eyikeyi rara rara
    • Rara, o ṣe itọsi aṣẹ isọpọ nipa lilo awọn iboju iparada lakoko titọju awọn ipo
  5. Iru faaji wo ni o ṣe iranṣẹ bi eegun ẹhin XLNet fun agbegbe gigun-gun?
    • Ayipada-XL
    • LSTM
    • Atilẹba fanila Amunawa
    • Convolutional nẹtiwọki
  6. How does XLNet combine bidirectional context with autoregressive prediction?
    • It predicts all tokens independently with no attention mask
    • It predicts in sampled factorization orders without inserting a [MASK] input token, using attention masks
    • It physically shuffles the sentence before every prediction
    • It replaces autoregressive prediction with a denoising autoencoder
  7. For a token sequence A B C, suppose a sampled factorization order predicts C before A and B. What context is available when predicting C?
    • The tokens A and B must always be provided first
    • Only the next sentence is available
    • No earlier tokens in that sampled order, although C remains at its original input position
    • The input sequence is physically reordered to C A B
  8. How does XLNet prevent the target token from leaking into its own prediction query?
    • It removes the target from the entire training sequence
    • It uses the target’s label as an input embedding
    • It replaces the token with a [MASK] symbol
    • The query stream represents the target position without its token content