ELECTRA Pretraining adanwo

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

1. Iṣẹ-ṣiṣe ikẹkọ iṣaaju wo ni ELECTRA nlo dipo awoṣe ede ti ko boju mu?
   • Rọpo àmi erin
   • Next gbolohun asọtẹlẹ
   • Autoregressive tókàn-ọrọ asọtẹlẹ
   • Awoṣe ede Permutation
2. Kini idi ti ELECTRA jẹ iṣiro-daradara ju BERT?
   • O nlo awọn fokabulari ti o kere pupọ
   • O ṣe iṣiro ifihan agbara ẹkọ lori gbogbo awọn ami-ami, kii ṣe awọn ti o boju-boju nikan
   • O ṣe ikẹkọ nikan lori 15% boju-boju ti awọn ami
   • O foju ẹrọ akiyesi patapata
3. Kini ipa wo ni nẹtiwọọki monomono kekere ṣe ni ELECTRA?
   • O ṣe ipo awọn abajade ipari nipasẹ didara
   • O compresses awoṣe lẹhin ikẹkọ
   • O ṣe agbejade awọn ami aropo ti o ṣeeṣe fun eleyatọ lati ṣe idajọ
   • O tumọ ọrọ laarin awọn ede
4. Kini yoo ṣẹlẹ si monomono lẹhin ikẹkọ iṣaaju ti pari?
   • O ti wa ni itanran-aifwy fun iṣẹ-ṣiṣe ni isalẹ
   • O ti wa ni dapọ pẹlu awọn discriminator
   • O di oluyipada fun iran
   • O ti wa ni asonu; nikan ni discriminator lo
5. ELECTRA ti ni idagbasoke nipasẹ awọn oniwadi lati awọn ẹgbẹ wo?
   • Google ati Stanford
   • Meta AI ati NYU
   • OpenAI ati Microsoft
   • DeepMind ati Oxford
6. Eyi ti nigbamii awoṣe tesiwaju ELECTRA ká rọpo-àmi-iwari ero?
   • GPT-2
   • DeBERTa-v3
   • T5
   • Ọrọ2Vec
7. What happens if ELECTRA’s generator proposes the original token at a position?
   • It is always labeled replaced because the generator proposed it
   • The position is removed from the input
   • That token is treated as original by the discriminator’s replaced-token label
   • The whole sequence is discarded
8. When comparing ELECTRA with another pretraining method, what makes a compute claim meaningful?
   • Compare model names alone
   • Assume every ELECTRA run uses one fixed compute ratio
   • Ignore generator training cost
   • Specify model size, data, training budget, and evaluation setting